{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MeterGroup, ElecMeter, selection and basic statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All NILM datasets consists of various groupings of electricity meters.  We can group the meters by house.  Or by the type of appliance they are directly connected to.  Or by sample rate.  Or by whether the meter is a whole-house \"site meter\" or an appliance-level submeter, or a circuit-level submeter.\n",
    "\n",
    "In NILMTK, one of the key classes is `MeterGroup` which stores a list of `meters` and allows us to select a subset of meters, aggregate power from all meters and many other functions.\n",
    "\n",
    "When we first open a `DataSet`, NILMTK creates several `MeterGroup` objects.  There's `nilmtk.global_meter_group` which holds every meter currently loaded (including from multiple datasets if you have opened more than one dataset).  There is also one `MeterGroup` per building (which live in the `Building.elec` attribute).  We also nested `MeterGroups` for aggregating together split-phase mains, 3-phase mains and dual-supply (240 volt) appliances in North American and Canadian datasets.  For example, here is the `MeterGroup` for building 1 in REDD:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: If you are on Windows, remember to escape the back-slashes, use forward-slashs, or use raw-strings when passing paths in Python, e.g. one of the following would work:\n",
    "\n",
    "```python\n",
    "redd = DataSet('c:\\\\data\\\\redd.h5')\n",
    "redd = DataSet('c:/data/redd.h5')\n",
    "redd = DataSet(r'c:\\data\\redd.h5')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  )\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import nilmtk\n",
    "from nilmtk import DataSet, MeterGroup\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "rcParams['figure.figsize'] = (13, 10)\n",
    "\n",
    "redd = DataSet('/data/redd.h5')\n",
    "elec = redd.buildings[1].elec\n",
    "elec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that there are two nested `MeterGroups`: one for the electric oven, and one for the washer dryer (both of which are 240 volt appliances and have two meters per appliance):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[MeterGroup(meters=\n",
       "   ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "   ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       " ), MeterGroup(meters=\n",
       "   ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "   ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       " )]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.nested_metergroups()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting these meters into a `MeterGroup` allows us to easily sum together the power demand recorded by both meters to get the total power demand for the entire appliance (but it's also very easy to see the individual meter power demand too)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily get a MeterGroup of either the submeters or the mains:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily get the power data for both mains meters summed together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2011-04-18 09:22:09-04:00    342.820007\n",
       "2011-04-18 09:22:10-04:00    344.559998\n",
       "2011-04-18 09:22:11-04:00    345.140015\n",
       "2011-04-18 09:22:12-04:00    341.679993\n",
       "2011-04-18 09:22:13-04:00    341.029999\n",
       "Name: (power, apparent), dtype: float32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains().power_series_all_data().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  )\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.submeters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats for MeterGroups"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Proportion of energy submetered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's work out the proportion of energy submetered in REDD building 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running MeterGroup.proportion_of_energy_submetered...\n",
      "Calculating total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...   "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\nilmtk\\nilmtk\\metergroup.py:935: UserWarning: As a quick implementation we only get Good Sections from the first meter in the meter group.  We should really return the intersection of the good sections for all meters.  This will be fixed...\n",
      "  warnings.warn(\"As a quick implementation we only get Good Sections from\"\n",
      "c:\\nilmtk\\nilmtk\\electric.py:307: UserWarning: No shared AC types.  Using 'active' for submeter and 'apparent' for other.\n",
      "  \" and '{:s}' for other.\".format(ac_type, other_ac_type))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...    total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...    total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...    total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...   "
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7599031850888346"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.proportion_of_energy_submetered()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that NILMTK has raised a warning that Mains uses a different type of power measurement than all the submeters, so it's not an entirely accurate comparison.  Which raises the question: which type of power measurements are used for the mains and submeters?  Let's find out..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Active, apparent and reactive power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mains = elec.mains()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apparent']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mains.available_ac_types('power')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['active']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.submeters().available_ac_types('power')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')      dataset='REDD'), ElecMeterID(instance=4, building=1, dataset='REDD')))     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')      dataset='REDD'), ElecMeterID(instance=20, building=1, dataset='REDD')))      ElecMeterID(instance=10, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "\n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>physical_quantity</th>\n",
       "      <th colspan=\"2\" halign=\"left\">power</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th>active</th>\n",
       "      <th>apparent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:09-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>344.173340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:12-04:00</th>\n",
       "      <td>261.0</td>\n",
       "      <td>341.170013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:15-04:00</th>\n",
       "      <td>261.0</td>\n",
       "      <td>341.973328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:18-04:00</th>\n",
       "      <td>262.0</td>\n",
       "      <td>341.683319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:21-04:00</th>\n",
       "      <td>262.0</td>\n",
       "      <td>341.796661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:24-04:00</th>\n",
       "      <td>261.0</td>\n",
       "      <td>341.330017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:27-04:00</th>\n",
       "      <td>261.0</td>\n",
       "      <td>341.223328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:30-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>341.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:33-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>341.503326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:36-04:00</th>\n",
       "      <td>262.0</td>\n",
       "      <td>342.196655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:39-04:00</th>\n",
       "      <td>266.0</td>\n",
       "      <td>345.356689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:42-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>342.736664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:45-04:00</th>\n",
       "      <td>261.0</td>\n",
       "      <td>342.610016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:48-04:00</th>\n",
       "      <td>261.0</td>\n",
       "      <td>342.683350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:51-04:00</th>\n",
       "      <td>267.0</td>\n",
       "      <td>344.553345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:54-04:00</th>\n",
       "      <td>262.0</td>\n",
       "      <td>341.613342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:22:57-04:00</th>\n",
       "      <td>264.0</td>\n",
       "      <td>344.770020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:00-04:00</th>\n",
       "      <td>267.0</td>\n",
       "      <td>345.350006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:03-04:00</th>\n",
       "      <td>266.0</td>\n",
       "      <td>344.976654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:06-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>343.896667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:09-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>344.713318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:12-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>344.549988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:15-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>345.026672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:18-04:00</th>\n",
       "      <td>260.0</td>\n",
       "      <td>345.353333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:21-04:00</th>\n",
       "      <td>264.0</td>\n",
       "      <td>344.380005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:24-04:00</th>\n",
       "      <td>264.0</td>\n",
       "      <td>345.903320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:27-04:00</th>\n",
       "      <td>263.0</td>\n",
       "      <td>344.033325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:30-04:00</th>\n",
       "      <td>263.0</td>\n",
       "      <td>346.293335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:33-04:00</th>\n",
       "      <td>266.0</td>\n",
       "      <td>345.863342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-18 09:23:36-04:00</th>\n",
       "      <td>267.0</td>\n",
       "      <td>345.149994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:33-04:00</th>\n",
       "      <td>246.0</td>\n",
       "      <td>274.713348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:36-04:00</th>\n",
       "      <td>247.0</td>\n",
       "      <td>277.260010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:39-04:00</th>\n",
       "      <td>247.0</td>\n",
       "      <td>276.209991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:42-04:00</th>\n",
       "      <td>240.0</td>\n",
       "      <td>274.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:45-04:00</th>\n",
       "      <td>240.0</td>\n",
       "      <td>274.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:48-04:00</th>\n",
       "      <td>244.0</td>\n",
       "      <td>275.653320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:51-04:00</th>\n",
       "      <td>246.0</td>\n",
       "      <td>277.756653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:54-04:00</th>\n",
       "      <td>242.0</td>\n",
       "      <td>275.173340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:55:57-04:00</th>\n",
       "      <td>240.0</td>\n",
       "      <td>274.473328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:00-04:00</th>\n",
       "      <td>240.0</td>\n",
       "      <td>273.679993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:03-04:00</th>\n",
       "      <td>243.0</td>\n",
       "      <td>272.766663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:06-04:00</th>\n",
       "      <td>241.0</td>\n",
       "      <td>273.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:09-04:00</th>\n",
       "      <td>242.0</td>\n",
       "      <td>273.729980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:12-04:00</th>\n",
       "      <td>245.0</td>\n",
       "      <td>273.609985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:15-04:00</th>\n",
       "      <td>243.0</td>\n",
       "      <td>273.743347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:18-04:00</th>\n",
       "      <td>242.0</td>\n",
       "      <td>274.056671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:21-04:00</th>\n",
       "      <td>240.0</td>\n",
       "      <td>273.526672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:24-04:00</th>\n",
       "      <td>240.0</td>\n",
       "      <td>274.350006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:27-04:00</th>\n",
       "      <td>243.0</td>\n",
       "      <td>274.796692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:30-04:00</th>\n",
       "      <td>242.0</td>\n",
       "      <td>274.220001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:33-04:00</th>\n",
       "      <td>241.0</td>\n",
       "      <td>273.739990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:36-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>274.326660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:39-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>275.693329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:42-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>273.793335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:45-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>273.366669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:48-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>273.266663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:51-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>274.496674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:54-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>273.959991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:56:57-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>273.636658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-24 15:57:00-04:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>273.825012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1044698 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "physical_quantity          power            \n",
       "type                      active    apparent\n",
       "2011-04-18 09:22:09-04:00    NaN  344.173340\n",
       "2011-04-18 09:22:12-04:00  261.0  341.170013\n",
       "2011-04-18 09:22:15-04:00  261.0  341.973328\n",
       "2011-04-18 09:22:18-04:00  262.0  341.683319\n",
       "2011-04-18 09:22:21-04:00  262.0  341.796661\n",
       "2011-04-18 09:22:24-04:00  261.0  341.330017\n",
       "2011-04-18 09:22:27-04:00  261.0  341.223328\n",
       "2011-04-18 09:22:30-04:00  260.0  341.250000\n",
       "2011-04-18 09:22:33-04:00  260.0  341.503326\n",
       "2011-04-18 09:22:36-04:00  262.0  342.196655\n",
       "2011-04-18 09:22:39-04:00  266.0  345.356689\n",
       "2011-04-18 09:22:42-04:00  260.0  342.736664\n",
       "2011-04-18 09:22:45-04:00  261.0  342.610016\n",
       "2011-04-18 09:22:48-04:00  261.0  342.683350\n",
       "2011-04-18 09:22:51-04:00  267.0  344.553345\n",
       "2011-04-18 09:22:54-04:00  262.0  341.613342\n",
       "2011-04-18 09:22:57-04:00  264.0  344.770020\n",
       "2011-04-18 09:23:00-04:00  267.0  345.350006\n",
       "2011-04-18 09:23:03-04:00  266.0  344.976654\n",
       "2011-04-18 09:23:06-04:00  260.0  343.896667\n",
       "2011-04-18 09:23:09-04:00  260.0  344.713318\n",
       "2011-04-18 09:23:12-04:00  260.0  344.549988\n",
       "2011-04-18 09:23:15-04:00  260.0  345.026672\n",
       "2011-04-18 09:23:18-04:00  260.0  345.353333\n",
       "2011-04-18 09:23:21-04:00  264.0  344.380005\n",
       "2011-04-18 09:23:24-04:00  264.0  345.903320\n",
       "2011-04-18 09:23:27-04:00  263.0  344.033325\n",
       "2011-04-18 09:23:30-04:00  263.0  346.293335\n",
       "2011-04-18 09:23:33-04:00  266.0  345.863342\n",
       "2011-04-18 09:23:36-04:00  267.0  345.149994\n",
       "...                          ...         ...\n",
       "2011-05-24 15:55:33-04:00  246.0  274.713348\n",
       "2011-05-24 15:55:36-04:00  247.0  277.260010\n",
       "2011-05-24 15:55:39-04:00  247.0  276.209991\n",
       "2011-05-24 15:55:42-04:00  240.0  274.000000\n",
       "2011-05-24 15:55:45-04:00  240.0  274.250000\n",
       "2011-05-24 15:55:48-04:00  244.0  275.653320\n",
       "2011-05-24 15:55:51-04:00  246.0  277.756653\n",
       "2011-05-24 15:55:54-04:00  242.0  275.173340\n",
       "2011-05-24 15:55:57-04:00  240.0  274.473328\n",
       "2011-05-24 15:56:00-04:00  240.0  273.679993\n",
       "2011-05-24 15:56:03-04:00  243.0  272.766663\n",
       "2011-05-24 15:56:06-04:00  241.0  273.660004\n",
       "2011-05-24 15:56:09-04:00  242.0  273.729980\n",
       "2011-05-24 15:56:12-04:00  245.0  273.609985\n",
       "2011-05-24 15:56:15-04:00  243.0  273.743347\n",
       "2011-05-24 15:56:18-04:00  242.0  274.056671\n",
       "2011-05-24 15:56:21-04:00  240.0  273.526672\n",
       "2011-05-24 15:56:24-04:00  240.0  274.350006\n",
       "2011-05-24 15:56:27-04:00  243.0  274.796692\n",
       "2011-05-24 15:56:30-04:00  242.0  274.220001\n",
       "2011-05-24 15:56:33-04:00  241.0  273.739990\n",
       "2011-05-24 15:56:36-04:00    NaN  274.326660\n",
       "2011-05-24 15:56:39-04:00    NaN  275.693329\n",
       "2011-05-24 15:56:42-04:00    NaN  273.793335\n",
       "2011-05-24 15:56:45-04:00    NaN  273.366669\n",
       "2011-05-24 15:56:48-04:00    NaN  273.266663\n",
       "2011-05-24 15:56:51-04:00    NaN  274.496674\n",
       "2011-05-24 15:56:54-04:00    NaN  273.959991\n",
       "2011-05-24 15:56:57-04:00    NaN  273.636658\n",
       "2011-05-24 15:57:00-04:00    NaN  273.825012\n",
       "\n",
       "[1044698 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(elec.load())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating total_energy for ElecMeterID(instance=2, building=1, dataset='REDD') ...   "
     ]
    },
    {
     "data": {
      "text/plain": [
       "apparent    167.766283\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains().total_energy() # returns kWh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy per submeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/16 MeterGroup(meters==19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])e=1)])ce=1)])\n",
      "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "16/16 MeterGroup(meters= for ElecMeterID(instance=3, building=1, dataset='REDD') ...    total_energy for ElecMeterID(instance=4, building=1, dataset='REDD') ...   \n",
      "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "Calculating total_energy for ElecMeterID(instance=20, building=1, dataset='REDD') ...   "
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>(5, 1, REDD)</th>\n",
       "      <th>(6, 1, REDD)</th>\n",
       "      <th>(7, 1, REDD)</th>\n",
       "      <th>(8, 1, REDD)</th>\n",
       "      <th>(9, 1, REDD)</th>\n",
       "      <th>(11, 1, REDD)</th>\n",
       "      <th>(12, 1, REDD)</th>\n",
       "      <th>(13, 1, REDD)</th>\n",
       "      <th>(14, 1, REDD)</th>\n",
       "      <th>(15, 1, REDD)</th>\n",
       "      <th>(16, 1, REDD)</th>\n",
       "      <th>(17, 1, REDD)</th>\n",
       "      <th>(18, 1, REDD)</th>\n",
       "      <th>(19, 1, REDD)</th>\n",
       "      <th>(((3, 1, REDD), (4, 1, REDD)),)</th>\n",
       "      <th>(((10, 1, REDD), (20, 1, REDD)),)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>active</th>\n",
       "      <td>44.750925</td>\n",
       "      <td>19.920875</td>\n",
       "      <td>16.786282</td>\n",
       "      <td>22.939649</td>\n",
       "      <td>30.734511</td>\n",
       "      <td>16.890262</td>\n",
       "      <td>5.221226</td>\n",
       "      <td>0.096302</td>\n",
       "      <td>0.411592</td>\n",
       "      <td>4.507334</td>\n",
       "      <td>2.256583</td>\n",
       "      <td>18.288595</td>\n",
       "      <td>11.811224</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>8.81796</td>\n",
       "      <td>32.614809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apparent</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          (5, 1, REDD)  (6, 1, REDD)  (7, 1, REDD)  (8, 1, REDD)  \\\n",
       "active       44.750925     19.920875     16.786282     22.939649   \n",
       "apparent           NaN           NaN           NaN           NaN   \n",
       "reactive           NaN           NaN           NaN           NaN   \n",
       "\n",
       "          (9, 1, REDD)  (11, 1, REDD)  (12, 1, REDD)  (13, 1, REDD)  \\\n",
       "active       30.734511      16.890262       5.221226       0.096302   \n",
       "apparent           NaN            NaN            NaN            NaN   \n",
       "reactive           NaN            NaN            NaN            NaN   \n",
       "\n",
       "          (14, 1, REDD)  (15, 1, REDD)  (16, 1, REDD)  (17, 1, REDD)  \\\n",
       "active         0.411592       4.507334       2.256583      18.288595   \n",
       "apparent            NaN            NaN            NaN            NaN   \n",
       "reactive            NaN            NaN            NaN            NaN   \n",
       "\n",
       "          (18, 1, REDD)  (19, 1, REDD)  (((3, 1, REDD), (4, 1, REDD)),)  \\\n",
       "active        11.811224       0.000085                          8.81796   \n",
       "apparent            NaN            NaN                              NaN   \n",
       "reactive            NaN            NaN                              NaN   \n",
       "\n",
       "          (((10, 1, REDD), (20, 1, REDD)),)  \n",
       "active                            32.614809  \n",
       "apparent                                NaN  \n",
       "reactive                                NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy_per_meter = elec.submeters().energy_per_meter() # kWh, again\n",
    "energy_per_meter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "column headings are the ElecMeter instance numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `fraction_per_meter` does the same thing as `energy_per_submeter` but returns the fraction of energy per meter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select meters on the basis of their energy consumption"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a new MeterGroup which only contains the ElecMeters which used more than 20 kWh:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 1, REDD)                         44.750925\n",
       "(8, 1, REDD)                         22.939649\n",
       "(9, 1, REDD)                         30.734511\n",
       "(((10, 1, REDD), (20, 1, REDD)),)    32.614809\n",
       "Name: active, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# energy_per_meter is a DataFrame where each row is a \n",
    "# power type ('active', 'reactive' or 'apparent').\n",
    "# All appliance meters in REDD are record 'active' so just select\n",
    "# the 'active' row:\n",
    "energy_per_meter = energy_per_meter.loc['active']\n",
    "more_than_20 = energy_per_meter[energy_per_meter > 20]\n",
    "more_than_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([                       (5, 1, 'REDD'),\n",
       "                              (8, 1, 'REDD'),\n",
       "                              (9, 1, 'REDD'),\n",
       "       (((10, 1, 'REDD'), (20, 1, 'REDD')),)],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instances = more_than_20.index\n",
    "instances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot fraction of energy consumption of each appliance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/16 MeterGroup(meters==19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])e=1)])ce=1)])\n",
      "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
      "16/16 MeterGroup(meters= for ElecMeterID(instance=4, building=1, dataset='REDD') ...   \n",
      "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
      "Calculating total_energy for ElecMeterID(instance=10, building=1, dataset='REDD') ...    total_energy for ElecMeterID(instance=20, building=1, dataset='REDD') ...   "
     ]
    }
   ],
   "source": [
    "fraction = elec.submeters().fraction_per_meter().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create convenient labels\n",
    "labels = elec.get_labels(fraction.index)\n",
    "plt.figure(figsize=(10,30))\n",
    "fraction.plot(kind='pie', labels=labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw wiring diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the wiring diagram for the MeterGroup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<networkx.classes.digraph.DiGraph at 0x252ccf79668>,\n",
       " <matplotlib.axes._axes.Axes at 0x252cd2bf9b0>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elec.draw_wiring_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's not very pretty but it shows that meters (1,2) (the site meters) are upstream of all other meters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buildings in REDD have only two levels in their meter hierarchy (mains and submeters).  If there were more than two levels then it might be useful to get only the meters immediately downstream of mains:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.meters_directly_downstream_of_mains()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot appliances when they are in use "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252cd380588>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.set_palette(\"Set3\", n_colors=12)\n",
    "# Set a threshold to remove residual power noise when devices are off\n",
    "elec.plot_when_on(on_power_threshold = 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats and info for individual meters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ElecMeter` class represents a single electricity meter.  Each `ElecMeter` has a list of associated `Appliance` objects.  `ElecMeter` has many of the same stats methods as `MeterGroup` such as `total_energy` and `available_power_ac_types` and `power_series` and `power_series_all_data`.  We will now explore some more stats functions (many of which are also available on `MeterGroup`)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "fridge_meter = elec['fridge']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get upstream meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.upstream_meter() # happens to be the mains meter group!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata about the class of meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'model': 'eMonitor',\n",
       " 'manufacturer': 'Powerhouse Dynamics',\n",
       " 'manufacturer_url': 'http://powerhousedynamics.com',\n",
       " 'description': 'Measures circuit-level power demand.  Comes with 24 CTs. This FAQ page suggests the eMonitor measures real (active) power: http://www.energycircle.com/node/14103  although the REDD readme.txt says all channels record apparent power.\\n',\n",
       " 'sample_period': 3,\n",
       " 'max_sample_period': 50,\n",
       " 'measurements': [{'physical_quantity': 'power',\n",
       "   'type': 'active',\n",
       "   'upper_limit': 5000,\n",
       "   'lower_limit': 0}],\n",
       " 'wireless': False}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.device"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dominant appliance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the metadata specifies that a meter has multiple meters connected to it then one of those can be specified as the 'dominant' appliance, and this appliance can be retrieved with this method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Appliance(type='fridge', instance=1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.dominant_appliance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "active    44.750925\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.total_energy() # kWh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get good sections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot the raw power data then we see there is one large gap where, supposedly, the metering system was not working.  (if we were to zoom in then we'd see lots of smaller gaps too):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252cd2e6240>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fridge_meter.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can automatically identify the 'good sections' (i.e. the sections where every pair of consecutive samples is less than `max_sample_period` specified in the dataset metadata):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "good_sections = fridge_meter.good_sections(full_results=True)\n",
    "# specifying full_results=False would give us a simple list of \n",
    "# TimeFrames.  But we want the full GoodSectionsResults object so we can\n",
    "# plot the good sections..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x252d35f20b8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "good_sections.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue chunks show where the data is good.  The white gap is the large gap seen in the raw power data.  There are lots of smaller gaps that we cannot see at this zoom level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see the exact sections identified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[TimeFrame(start='2011-04-18 09:22:13-04:00', end='2011-04-18 14:00:33-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-18 14:03:11-04:00', end='2011-04-19 18:45:09-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-19 20:20:05-04:00', end='2011-04-20 01:54:26-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-20 01:55:33-04:00', end='2011-04-21 06:14:43-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-21 06:17:22-04:00', end='2011-04-21 17:45:02-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-21 19:41:23-04:00', end='2011-04-22 22:46:53-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-22 22:48:31-04:00', end='2011-04-24 03:48:44-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-24 03:52:28-04:00', end='2011-04-27 02:50:14-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-27 02:51:25-04:00', end='2011-04-27 03:17:31-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-27 03:21:19-04:00', end='2011-04-29 23:07:52-04:00', empty=False),\n",
       " TimeFrame(start='2011-04-29 23:10:38-04:00', end='2011-05-01 09:44:36-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-01 09:47:22-04:00', end='2011-05-02 17:04:54-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-02 17:07:31-04:00', end='2011-05-03 17:30:12-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-03 17:32:53-04:00', end='2011-05-03 17:33:17-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-06 10:51:50-04:00', end='2011-05-07 02:38:12-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-07 02:40:47-04:00', end='2011-05-08 13:32:42-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-08 13:33:34-04:00', end='2011-05-11 03:16:14-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-11 03:19:47-04:00', end='2011-05-12 17:48:31-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-12 20:14:27-04:00', end='2011-05-22 01:00:03-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-22 01:01:01-04:00', end='2011-05-22 16:03:54-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-22 16:04:50-04:00', end='2011-05-22 23:38:54-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-22 23:41:39-04:00', end='2011-05-23 09:22:00-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-23 10:31:31-04:00', end='2011-05-24 14:32:01-04:00', empty=False),\n",
       " TimeFrame(start='2011-05-24 15:55:38-04:00', end='2011-05-24 15:56:34-04:00', empty=False)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "good_sections.combined()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropout rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As well as large gaps appearing because the entire system is down, we also get frequent small gaps from wireless sensors dropping data.  This is sometimes called 'dropout'.  The dropout rate is a number between 0 and 1 which specifies the proportion of missing samples.  A dropout rate of 0 means no samples are missing.  A value of 1 would mean all samples are missing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21922786156570626"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridge_meter.dropout_rate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the dropout rate has gone down (which is good!) now that we are ignoring the gaps.  This value is probably more representative of the performance of the wireless system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select subgroups of meters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `ElecMeter.select_using_appliances()` to select a new MeterGroup using an metadata field.  For example, to get all the washer dryers in the whole of the REDD dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=7, building=2, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=13, building=3, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=14, building=3, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=7, building=4, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=8, building=5, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=9, building=5, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=4, building=6, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nilmtk\n",
    "nilmtk.global_meter_group.select_using_appliances(type='washer dryer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or select multiple appliance types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.select_using_appliances(type=['fridge', 'microwave'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or all appliances in the 'heating' category:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=10, building=3, dataset='REDD', appliances=[Appliance(type='electric furnace', instance=1)])\n",
       "  ElecMeter(instance=4, building=4, dataset='REDD', appliances=[Appliance(type='electric furnace', instance=1)])\n",
       "  ElecMeter(instance=6, building=5, dataset='REDD', appliances=[Appliance(type='electric furnace', instance=1)])\n",
       "  ElecMeter(instance=12, building=5, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=13, building=5, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=12, building=6, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nilmtk.global_meter_group.select_using_appliances(category='heating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or all appliances in building 1 with a single-phase induction motor(!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nilmtk.global_meter_group.select_using_appliances(building=1, category='single-phase induction motor')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(NILMTK imports the 'common metadata' from the NILM Metadata project, which includes a wide range of different [category taxonomies](http://nilm-metadata.readthedocs.org/en/latest/central_metadata.html#appliancetype))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=7, building=2, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  ElecMeter(instance=10, building=2, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=11, building=2, dataset='REDD', appliances=[Appliance(type='waste disposal unit', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nilmtk.global_meter_group.select_using_appliances(building=2, category='laundry appliances')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select a group of meters from properties of the meters (not the appliances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.select(device_model='REDD_whole_house')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  )\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.select(sample_period=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select a single meter from a MeterGroup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `[]` to retrive a single `ElecMeter` from a `MeterGroup`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for a meter using appliances connected to each meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec['fridge']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Appliances are uniquely identified within a building by a `type` (fridge, kettle, television, etc.) and an `instance` number.  If we do not specify an instance number then `ElecMeter` retrieves instance 1 (instance numbering starts from 1).  If you want a different instance then just do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.select_using_appliances(type='fridge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec['light', 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To uniquely identify an appliance in `nilmtk.global_meter_group` then we must specify the dataset name, building instance number, appliance type and appliance instance in a dict:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nilmtk\n",
    "nilmtk.global_meter_group[{'dataset': 'REDD', 'building': 1, 'type': 'fridge', 'instance': 1}]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Search for a meter using details of the ElecMeter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "get ElecMeter with instance = 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instance numbering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ElecMeter and Appliance instance numbers uniquely identify the meter or appliance type within the *building*, not globally.  To uniquely identify a meter globally, we need three keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nilmtk.elecmeter import ElecMeterID \n",
    "# ElecMeterID is a namedtuple for uniquely identifying each ElecMeter\n",
    "\n",
    "nilmtk.global_meter_group[ElecMeterID(instance=8, building=1, dataset='REDD')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select nested MeterGroup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select a single, existing nested MeterGroup.  There are two ways to specify a nested MeterGroup:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[[ElecMeterID(instance=3, building=1, dataset='REDD'), \n",
    "      ElecMeterID(instance=4, building=1, dataset='REDD')]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       ")"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[ElecMeterID(instance=(3,4), building=1, dataset='REDD')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify the mains by asking for meter instance 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       ")"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec[ElecMeterID(instance=0, building=1, dataset='REDD')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which is equivalent to elec.mains():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.mains() == elec[ElecMeterID(instance=0, building=1, dataset='REDD')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot sub-metered data for a single day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=3, building=1, dataset='REDD')      ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "image/png": 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9tGjRwmuvQUoJrVMhIiIiLqQpZW8hjz/+ONnZ2URFRTFnzhxCQ0MBuP3223nuuee4//776d+/P40bN6ZChQoAjBs3jtTUVKKiorj33ntZunSpN1+ClBaFYyo0payIiIi4gMW4Vt+ZMuTYsWNFHufk5Fyzm9GtJjs7m4CAAPLz83nqqafo378/3bt392gd7HY7+fn5Hj2nJ5Wmz0xISAinTp266ec7PliE8flHWB58Amv3h1xYs7KnpLGW66dYe45i7RmKs+co1p5Ro0aNYvep+1MZMW3aNDZv3kxeXh6dO3d2zgwlclWa/UlERERcSElFGTF27FhvV0FuJc6B2pr9SUREREpOYypEzMg5paxaKkRERKTklFSImJFmfxIREREXUlIhYkaFYyq0ToWIiIi4gJIKETNyNlQoqRAREZGSU1LhRbVr1yY6OpouXboQFRXF/PnzcfwycDY1NZUxY8YU+9yUlBQef/xxT1XVa+cUN9HsTyIiIuJCmv3Ji/z8/EhKSgLg1KlTDBs2jPPnz/Piiy/SsmVLWrZs6eUaulZ+fj52uz5ypYJz9iclFSIiIlJyaqkoJUJCQnjzzTdZtGgRhmEUaRX4+uuviY6OJjo6mpiYGLKysoDLi7ENGTKETp06ER8fz+/XMTx16pRzvYq9e/dSs2ZNjh49CkB4eDgXLlxg3bp13H///cTExNCvXz9Onjx5U+fcvXs3DzzwAN26dWPAgAGkp6cD0KdPHyZOnMhDDz3EO++84+Yoyg0zNKWsiIiIlJxuG5cidevWxTCMK1aEnDdvHhMmTKBdu3ZkZ2fj6+sLwJ49e9iwYQPVq1end+/ebNu2jfbt2zufFxISQl5eHufPn2fr1q20bNmSLVu20L59e6pUqYK/vz/t27dnzZo1WCwW/ud//oc5c+bwyiuv3NA5W7VqxejRo1myZAmVK1dm1apVTJ48mbfeeguAc+fOsWLFCg9FUa6P8bt/RURERG6ekgpg06ZNzjv0rlK1alU6dep0w8/7fWsDQLt27Xjttdf485//TPfu3Z1LpIeGhjp/vvPOO0lLSyuSVAC0bduWbdu28c033/Dcc8/xxRdfYBgGYWFhABw/fpyhQ4dy4sQJLl68SJ06dW74nBUrVuT777/n4YcfxjAMHA4H1apVc9YhNjb2huMgblb4MVP3JxEREXEBdX8qRX788UesVishISFFtsfHxzNlyhRyc3Pp1asXBw4cAKBcuXLOY2w2G/n5+VeU2b59e7Zs2cLRo0e577772LdvH1u3bnUmFWPGjGHQoEGsX7+eyZMnk5eXd8PnNAyDJk2asGHDBpKSkli/fj3Lli1zHle+fHkXRUhcpjB51exPIiIi4gJqqYCbalFwtdOnT/PSSy8xaNAgLM6FyS47cuQIzZo1o1mzZuzYsYMDBw5QsWLF6yq3Q4cOvPnmm3To0AGr1UpQUBAbNmwgISEBuNw1qXr16gAsX778ps7ZsGFDMjIynF2hLl26xKFDh2jatOnNhEI8Qt2fRERExHWUVHhRbm4u0dHR5OfnY7PZ6NOnD08//fQVx73zzjukpKRgtVpp0qQJXbp0YceOHdd1jtq1awM4WybatWvH8ePHqVy5MgD/9V//RVxcHNWrV6d169akpaXd8DnLlSvH/PnzeeWVVzh79iwFBQUMHjxYSUVp5uz+pIHaIiIiUnIW42qd+MugY8eOFXmck5OjbjkuZrfbr9oFq6woTZ+ZkJCQKwb03wjHqn9hfJyIJbIX1v5DXFizsqeksZbrp1h7jmLtGYqz5yjWnlE4rvZqNKZCxIwK7yU4CrxbDxERESkTlFSImJm6P4mIiIgLKKkQMSPnOG1T9H4UERERN1NSIWJKhd2f1FIhIiIiJaekQsSMnOtUKKkQERGRkvPIlLJz5sxh586dVKpUiWnTpgEwffp054xMhbPqTJkyhRMnTjB8+HDn6PLGjRs7p1k9dOgQs2fP5uLFi7Rq1eqqazqIyPVQS4WIiIi4jkeSinvvvZdu3boxe/Zs57bhw4c7f16yZEmRqTqrV6/OlClTrihn4cKFxMXF0bhxYyZOnMiuXbto1aqVeyvvRjNnzmTlypXYbDYsFguTJ0+mdevW1/38xMREdu/ezfjx46/r+JSUFHx8fGjXrt3NVlnKCq1TISIiIi7kkaSiefPmnDhx4qr7DMPg66+/ZuzYsdcsIzMzkwsXLtCkSRPg8irYhSs434q2b99OcnIya9euxdfXl4yMDC5evOjWc3799dcEBAQoqZDfdH/SQG0REREpOa+vqP3dd99RqVIlbr/9due2EydOMHLkSPz9/enfvz/NmjUjIyODKlWqOI+pUqUKGRkZ3qiyS5w4cYLg4GB8fX0BCA4OBmDz5s2MGzeOgoICWrZsycSJE/H19WXXrl2MHTuWnJwcfH19SUxMLFJecnIyM2fO5B//+AeGYfDSSy9x9OhRAF577TWqV6/O0qVLsdlsrFixgjfeeIMTJ04wffp0rFYrFStW5MMPP/RsEMSL1P1JREREXMfrScX//u//8qc//cn5OCgoiDlz5lChQgUOHTrElClTmDZtGje68HdycjLJyckATJo0iZCQkCL709PTsdu99/IjIyOZMWMGHTt2pFOnTvTu3ZvWrVszYsQIPvjgAxo2bEh8fDz//Oc/GTRoEEOHDmXBggW0atWK8+fP4+/vj81mw2q1sm7dOubNm8eyZcuoXLkyzzzzDM888wxhYWH8/PPP9O/fn6+++oonnniCgIAAnn32WQA6d+5MYmIit99+O2fPnnVJPLwZU3fz9fW94nPkLXa7vUR1Oe/nTw5QzsdO5VLymkqrksZarp9i7TmKtWcozp6jWHufV78BFhQUsHXrViZNmuTc5uPjg4+PDwANGjTgtttu4/jx41SpUoXTp087jzt9+rTz7v7VREVFERUV5Xz8+6Xb8/LysNlsAASeXIM977hLXlOhfN/byaraq9j9vr6+fPbZZ2zZsoWUlBSefvpp4uPjqV27NnXr1iU/P5+HHnqIf/zjH4SHh1OtWjXuuusu8vPz8ff3By7H76uvvuLf//43y5YtIzAwkPz8fDZt2sT333/vPNf58+c5c+YMDocDh8NBfn4+AG3btuW5556jV69edO/e3bn9Ztnt9hKXUZrl5eVd8TnylpCQkBLVxXEhB4C83NxS85pKq5LGWq6fYu05irVnKM6eo1h7RuFESlfj1aTi22+/pUaNGkW6NZ07d47AwECsVivp6ekcP36c2267jcDAQPz9/dm/fz+NGzdm06ZNdOvWzYu1LzmbzUZ4eDjh4eHccccdLF++/KrHGYZR7CxXderU4aeffuLQoUO0bNkSAIfDwerVq53JR3EmT57Mzp07Wb9+PTExMaxbt+6aiZqUIYa6P4mIiIjreCSpmDFjBvv27eP8+fM888wzPPzww3Tt2vWKrk8A+/bt4/3333d27RkyZAiBgYEADB48mDlz5nDx4kVCQ0NdNkj7Wi0K7nLgwAGsVisNGjQAYO/evVStWpV9+/Zx+PBh6tevz4oVK+jQoQONGjUiPT2dXbt2ERoaSlZWFn5+fgDUqlWLMWPGMHjwYObPn0/Tpk3p3LkzixcvZujQoQDs2bOHFi1aEBAQQFZWlrMOR44coXXr1rRu3ZqkpCSOHTumpMIsNPuTiIiIuJBHkooXXnjhqtuHDRt2xbYOHTrQoUOHqx7fsGFD5zoXt7qcnBxGjx7NuXPnsNvt1KtXjzfffJPevXsTFxfnHKg9cOBAypUrx9y5cxk9ejS5ubn4+fkVGajdqFEjZs2aRVxcHIsXL2bcuHGMGjWKqKgo8vPzCQsLY/LkyURHRxMXF8fnn3/OG2+8wcKFCzl8+DCGYRAREcGdd97pxYiIZ2n2JxEREXEdi3GjI6BvUYUL7RUqXHBPXKesj6koTZ+ZEo+peG8hxvo10LwVtuGvubBmZY/66XqOYu05irVnKM6eo1h7xrXGVFg9WA8RKW0MdX8SERGRklNSIWJGGqgtIiJuYqQdxvjPUW9XQzys7C4qICLFc66oraRCRERcy/H68wDYFq72ck3Ek9RSIWJKaqkQERER11FSIWJGhdMzmGOeBhEREXEzJRUipqSWChEREXEdJRVeNHPmTLp06UJUVBTR0dHs3Lnzhp6fmJjIyy+/fN3Hp6SksG3bthutppRFGqgtIiIiLqSB2l6yfft2kpOTWbt2Lb6+vmRkZHDx4kW3nvPrr78mICCAdu3aufU8cgtwdn9SUiEiIiIlp5YKLzlx4gTBwcH4+voCEBwcTPXq1dm8eTMxMTFERkYyYsQI8vLyANi1axexsbFERUXRs2dPsrKyipSXnJxMr169yMjI4PTp0wwZMoQePXrQo0cPtm3bRlpaGkuXLmXhwoVER0ezZcsW1qxZQ9euXYmKiuLBBx/0eAzEm9RSISIiIq6jlgov6dy5M9OnTyciIoKOHTsSGxtLq1atGD58OImJiTRs2JC//vWvLFmyhCeeeIKhQ4cyd+5cQkNDOX/+PH5+fs6yPvvsMxYsWMDSpUupXLkyw4YNY8iQIbRv356jR48yYMAANm7cyMCBAwkICOCZZ54BIDIykn/961/cfvvtnD171luhEG9Q9ycRERFxISUVwM7j/+RM7o8uLbOyX11a3/5YsfsDAgJYu3YtW7ZsISUlhaFDhxIfH0+dOnVo2LAhAH379uUf//gHERERVKtWjdDQUAAqVKjgLCclJYXU1FSWLVvm3L5582b279/vPCYrK+uKlg2Atm3bMnz4cHr16kX37t1d8rrlFqPZn0RERMQFlFR4kc1mIzw8nPDwcO644w6WL19+1eMMw8BisVx1X506dfjpp584dOgQLVu2BMDhcLB69Wr8/f2vef7Jkyezc+dO1q9fT0xMDOvWrSM4OLhkL0puDWqpEBERERdSUgHXbFFwlwMHDmC1WmnQoAEAe/fupWrVquzbt4/Dhw9Tv359VqxYQYcOHWjUqBHp6ens2rWL0NBQsrKynN2fatWqxZgxYxg8eDDz58+nadOmdO7cmcWLFzN06FAA9uzZQ4sWLQgICCjSYnHkyBFat25N69atSUpK4tixY0oqzMKZVBR4tx4iIiJSJiip8JKcnBxGjx7NuXPnsNvt1KtXjzfffJPevXsTFxdHQUEBLVu2ZODAgZQrV465c+cyevRocnNz8fPzIzEx0VlWo0aNmDVrFnFxcSxevJhx48YxatQooqKiyM/PJywsjMmTJxMdHU1cXByff/45b7zxBgsXLuTw4cMYhkFERAR33nmnFyMinvVLUqHuTyIiIuICFsMwx7eKY8eOFXmck5ND+fLlvVSbsslut5Ofn+/tarhNafrMhISEcOrUqZt+vmPRTIyU9RBcFdvkd11Ys7KnpLGW66dYe45i7RlmjXPBkFgAbAtXe+ycZo21p9WoUaPYfZpSVsSMDLVUiIiIiOsoqRAxJQ3UFhER1zN0XTEtJRUiZlTYQKGB2iIi4kq6rpiWkgoRU1L3JxFxH8ea93BsWuvtaog3qKXCtJRUiJiR1qkQETcydqZg7N7u7WqIN6ilwrSUVIiYUWEDhaGkQkTcQK2g5lWg64pZKanwosaNG1+xbcmSJcWurF0oMTGRl19++ar7/v73v7ukblLWFbZU6MIvIm6ixMKc1AJuWkoqSpnHH3+cvn373vTz3377bRfWRsos55SyaqYWETcwDCUVZqXuT6alpKKUmTZtGvPmzQNg165dREVF0atXL8aNG0fXrl2dx6Wnp/Poo4/ypz/9iTfeeAOACRMmkJubS3R0NPHx8V6pv9xidEdJRNxBCYV5FSipMCslFaXYiBEjmDhxImvWrMFmsxXZt3fvXubOncv69etZvXo1R48eZdSoUfj5+ZGUlMSsWbO8VGu5JWjxOxFxN43ZMie976Zl93YFSoOKPx/D50KuS8u85O/HuVrFL2X+R86ePUtWVhbt2rUD4IEHHiA5Odm5PyIigooVKwLQpEkTjh49Ss2aNUtWaTEPzf4kIu6mmxbmpJYK01JLRSll/MEf43Llyjl/tlqt5Ofnu7tKUoZcAvZWqg+G8YefNRGRG2YYv84yJ+aiMRWmpZYKKFGLgrtUrlyZwMBAdkUr1KcAACAASURBVOzYQZs2bVi1atV1Pc/Hx4dLly7h4+Pj5hrKrey9cs34sFVvJu14mzsMB1hsf/wkEZHrZqCswqQ0paxpKanwogsXLtCmTRvn46effrrI/qlTpzJy5Ej8/f0JDw+nQoUKf1jmo48+SlRUFHfddZfGVUix0qyXP0tnylW43AXKqqRCRFzIQN2fzEqzCpqWkgov+vnnn6+5v2nTps5xFLNmzaJly5YA9OvXj379+jmPW7JkifPnl19+udg1LEQKWX65g+iwWDSuQkREXEctFaalpKIUS05OZtasWRQUFFCzZk1mzJjh7SpJGWFx3kG0uP1uomEYGGuWYenUDUvlYLeeS0RKCcPQDQuz0pgK01JSUYr17t2b3r17e7saUgZZfvnXIy0Vh77HWPMexoHvsI0Y595ziUgpoa5PpqVk0rQ0+5OICVl+e8F395ziBb/MTJZ/yb3nEZHSQytqm5emlDUtJRUiJuQcU+GJPwGFXyws+nMjYi5KKkxJ3Z9MS1d5ETP6zZAKt1/3C5vCLZZrHyciZYdaKsxL3Z9MS2MqREzI6mypcF9WYRz8Pygf+OsXC6vuYYiYipIKc1JLhWnpKu9FNWvW5LnnnnM+zs/P56677uLxxx8HYN26dVprQtyisPuT4cbZnxyTRuIY++xvuj+ppULENNRSYV6aUta01FLhReXLl+f777/nwoUL+Pv7s2nTJqpXr+7cHxMTQ0xMzHWVZRgGhmFg1d1guQ6FU8oaFov7uz8Z6v4kImIaWvzOtPQN1Mu6dOnC+vXrAVi5ciUPPPCAc19iYqJzIbuTJ0/y1FNPERUVRVRUFNu2bSMtLY3OnTuTkJDAfffdx7Fjx1i5ciWRkZF07dqV8ePHA7B69WpeffVVAN555x3uueceAI4cOeI83/Tp0+nRowddu3Zl5MiRGIbBDz/8QM+ePZ31SUtLIyoqCoDdu3fz0EMP0a1bNwYMGEB6erp7AyUuVfj13vjN/91GA7VFzCfrPGSe9nYtxBsKWyp0k9N09I57We/evVm1ahW5ubl89913tGrV6qrHjRkzhg4dOpCcnMznn39O06ZNATh48CB9+vRh3bp12O12xo8fz/vvv8+6devYtWsXa9eupUOHDmzduhWALVu2EBQUxPHjx9m6dSthYWEA/OUvf+HTTz9lw4YNXLhwgaSkJBo3bszFixf58ccfgcvJyf3338+lS5cYPXo0CxYsYO3atfTr14/Jkyd7IFriKs7uT55oPdBAbRHzyb8IuTneroV4gVE4pkJJhemo+xPgeG8hRtphl5ZpqV0fa/8hf3hc8+bN+fnnn1m1ahVdu3Yt9rj//d//ZebMmQDYbDYqVqzI2bNnqVWrFm3atAEgNTWVe+65hypVqgDw4IMP8s0339CtWzeys7PJysri+PHjPPDAA2zZsoWtW7fSvXt3AFJSUpg7dy4XLlzgzJkzNG3alJiYGHr16sWaNWuIj49n9erVzJ07l4MHD/L999/Tv39/ABwOB9WqVStRvMSznN2fPLCitrP7ky4wIiJln0N/881KSUUpEBMTw+uvv84HH3xAZmbmDT23fPnyzp+Na3w5bNOmDYmJiTRo0ICwsDDee+89duzYwdixY8nNzWXUqFF8+umn1KxZk2nTppGXlwdAbGwscXFxdO/eHYvFQoMGDfjuu+9o0qQJa9asubkXLF5XZPE7d3d/cmigtojpGGigtlkVLn5nsXm3HuJxSirguloU3Klfv35UqFCBZs2akZKSctVjIiIiWLJkCUOGDKGgoICcnCublVu1asXYsWPJyMigUqVKrFy5kieffBKAsLAwpk6dyvDhw2nRogUpKSn4+fk5WzwAgoODyc7O5pNPPnGOpahXrx42m40ZM2YQGxsLQMOGDcnIyGD79u20bduWS5cucejQIe688053hEfcwLn4ncXqgYHamlJWRMQ01P3JtJRUlAI1atRg8ODB1zzm9ddfZ+TIkbz33ntYrVYmTpzIbbfdVuSY2267jYSEBPr27YthGHTt2pX77rsPuJxUHDt2jLCwMGw2GzVq1KBRo0YAVKpUiQEDBhAVFUWtWrVo2bJlkXJjY2MZN24c33zzDQDlypVj/vz5jB07lnPnzlFQUMDgwYOVVNxCLEUaKjzU/UktFSLmooYKc9I4OtOyGNfqM1OGHDt2rMjjnJycIl2HpOTsdjv5+fnerobblKbPTEhICKdOnbrp589auIak8o2J+34F3Uc8g6VyFRfW7rKCIZdbtixP/w1jwRQsbSOwxo10+XncraSxluunWHuOu2NdEPcAlPPF9nai285xKzDjZ9rx5acY/5oHFSphe2upx85rxlh7Q40aNYrdp7YpEROy/HZBOnffVtBdKxER89CUsqald1zEhJxjKvDAF32tUyFiTuboCCG/VzimQjeSTMcjYyrmzJnDzp07qVSpEtOmTQPg/fffZ/369VSsWBGARx55hNatWwPw0UcfsWHDBqxWK4MGDSI0NBSAXbt2sWjRIhwOB5GRkUUWihOR61dk9id3X/id0wvqAiMiUuZpSlnT8khSce+999KtWzdmz55dZHvPnj2dMwoV+vnnn0lJSeGtt94iMzOTcePGOddnePfddxk9ejRVqlQhISGBtm3bUqtWLU+8BJEypfDrvcNiwf0rahd2f9IFRsQ01EhhXs7ZnzSlrNl4JKlo3rw5J06cuK5jt23bRnh4OD4+PlSrVo3q1atz4MABAKpXr+6c8Sg8PJxt27YpqRC5CZbCL/p4YEzFpUuX/1VLhYi5qPuTORVoSlmz8uqUsp9//jmbNm2iQYMGPP744wQGBpKRkUHjxo2dxwQHB5ORkQHgXCm68OcffvjB43UWKQsKp5T1SEvFpYu/nFQXGBGRMs+h1mmz8lpSERMTQ58+fQBITExkyZIlPPvss8WuCn217ZZrDAJKTk4mOTkZgEmTJhESElJkf3p6Ona793Kqn376iccee4xNmzY5t02ZMoWAgACeffbZqz7nvffeIzU1lYkTJ3qqmjfMmzF1N19f3ys+R95it9tLVBebs9XAQnBQEDY3vK70X/4N8LGTBfj5+1OxlMTvRpQ01nL9FGvPcXes0wEsFtO/n2b8TGf5+ZIN2Gw2j752M8a6tPHaN8DKlSs7f46MjGTy5MnA5RaI06dPO/dlZGQQHBwMUGT76dOnCQoKKrb8qKgooqKinI9/P3dxXl4eNpv3+vsV/NI8+Nt1HRwOBw6Ho9i1HgoKCq6539vK+joVeXl5pWYO7JLOx2380ufVgYWM06exWH1cVbUrZJ/JBCA37yIXS0n8boTmPvccxdpz3B9rAxwO07+fZvxMO7LOA5e/s3jytZsx1t5QKtepyMzMdP68detWateuDUDbtm1JSUnh0qVLnDhxguPHj9OoUSMaNmzI8ePHOXHiBPn5+aSkpNC2bVtvVd+t+vTpw/jx4+nZsycRERFs2bLlimOSk5Pp1asXGRkZvPDCC4wZM4bY2FjuuecePv74Y+By6864cePo2rUrkZGRrFq1CoCEhATWrVsHwFNPPcWIESMAWLZsGZMnTyYtLY3OnTvzt7/9jS5duvDII49w4cIFD7168YTCdSoMTwxzKOz+pDEVIiJlX2H3J42pMR2PtFTMmDGDffv2cf78eZ555hkefvhh9u7dy5EjR7BYLFStWpWnn34agNq1a3PPPfcwYsQIrFYrTz31FNZfBvs8+eSTjB8/HofDQZcuXZyJSFmUn5/PJ598wvr163nrrbdITPx1VdLPPvuMBQsWsHTpUmeLT3p6OitXruTAgQMMGjSI+++/n08//ZS9e/eSlJRERkYGPXr0oEOHDnTo0IEtW7YQExPDf/7zH9LTL3dU2bp1K7179wbg8OHDzJ49mylTphAXF8enn37KQw895PlAiFsUTilreGKdiouFYyqUVIiIlHmFi99pCjDT8UhS8cILL1yxrWvXrsUe/+CDD/Lggw9esb1169bOtSxc6Z3t6RzOzHVpmfWD/Bjc9rZi919rPAhAjx49ALj77rv5+eefndtTUlJITU1l2bJlVKhQwbm9W7duWK1WmjRpwsmTJ4HLScIDDzyAzWajatWqdOjQgdTUVNq3b8/ChQvZv38/jRs35uzZs6Snp7Njxw7GjRtHZmYmtWvXpkWLFs46pKWl3XQspPRxtlRgcf/dJA3UFjEpfak0pcIpZdVSYTq6yntJUFAQZ8+eLbLtzJkzzvEj5cqVAy4PdPrtOIU6deqQnZ3NoUOHijy38Hj4dVB7cYPeb7/9ds6ePcsXX3xBhw4dCAsLY82aNQQEBBAYGAhcHpRcyGazOceASNlQmNIaHp39SS0VIqai75TmVNj9SUyn7E7VcwOu1aLgLgEBAVSrVo3NmzfTsWNHMjMz+eKLL3jqqad4//33i31erVq1GDNmDIMHD2b+/Pk0bdq02GM7dOjAP//5T/r27cuZM2fYsmULY8aMAaBNmza88847vP/++2RmZvL000/Ts2dPl79OKZ0K16kwnP9zH+OiWipEREzDoZuQZqWrvBfNnDmTmTNnEh0dzcMPP8yIESOoV6/eHz6vUaNGzJo1i7i4OI4cOVLscd27d6dZs2bO8l9++WWqVasGQFhYGAUFBdSvX5+77rqLM2fOEBYW5qJXJqVdkZYKt3d/yrv8rwZqi5iMmipMqUDdn8zKYhTXR6aMOXbsWJHHOTk5lC9f3ku1KZvK+pSypekzU9Kp85bO+h8+CGpN/8Of03/wQ1iqu35l+oIhsZd/aHIn7N+L5b4/Y+0zyOXncTdNU+g5irXnuDvWBUN6g92Gbe6HbjvHrcCMn2nHf8/A+HoDVKmGbdI7HjuvGWPtDaVySlkR8R7vzP6kPzci5mGoocKs1P3JtHSVFzEhZ1Jhsbj/wq+B2iIi5qGB2qalpELEhIpMKevurOLiL2Mq1FIhIlLmGZpS1rR0lRcxoV+7P+H+P/zO7k/uPY2IlDb6UmlKWvzOtJRUiJiQYVz+hu/W7k+F3Z0KWypExFz0ndKcnC0V3q2GeJ6SChET+iWncG/3J9svy+AUJhW6wIiIlH0aU2FaSiq8qHbt2kRHRzv/mzVrFgB9+vQhNTX1hsvbs2cP69evL3Z/amqqc/G7W8G0adOYN2+eS8pauHAhFy5ccElZZcIvX/Dduk5FYVJRUHanGRaRa9GdBHMqvMDo/TcbrajtRX5+fiQlJbmsvL1797J7924iIyOv2Jefn0/Lli1p2bKly853K3nnnXd46KGH8Pf3v+7nFBQUYLPZ3Fgr7yn8U+9w530F2+/L1gVGRKTMcyYT+ptvNkoqSrmNGzcydepULl68SN26dZk+fToBAQHs2rWLsWPHkpOTg6+vL8uWLWPq1Knk5uaydetW4uPjOXDgAOnp6aSlpREcHMyjjz7KvHnzWLJkCdnZ2YwePZrdu3djsVgYPnw4PXv2LHLuCRMmsG7dOux2O506dWLs2LG88MIL+Pr6sn//fk6ePMkrr7xCdHQ0aWlpPP/882RnZwPwxhtv0K5dOwDmzJnDihUrsFgsdO3alVGjRnHkyBFefvllTp8+jb+/P1OmTKFRo0ZXvP79+/fTp08fjh49yuDBg3nqqacAWLFiBf/93//NxYsXadWqFRMnTsRms/HSSy+RmppKbm4uPXv25MUXX+Tdd98lPT2dvn37EhQUxAcffFBsXMPCwujfvz8bN25k0KBB9O7d283vsHc4kwqLB2Z/cp5UFxgRkTJPOYVpKanwotzcXKKjo52P4+Pji3yJzcjIYObMmSQmJlK+fHlmz57NggULGDZsGEOHDmXu3LmEhoZy/vx5/P39efHFF9m9ezfjx48HLncf2r17Nx999BH+/v6kpKQ4y54xYwYVKlRwdpc6c+ZMkbplZmby2WefsWnTJiwWC2fPnnXu+/nnn1mxYgVHjhyhb9++dOzYkZCQEN5//33sdjuHDh1i2LBhfPbZZ2zYsIG1a9fy8ccf4+/vT2ZmJgAjR45k0qRJNGjQgJ07d5KQkMDy5cuviNGBAwdYvnw52dnZdOzYkccff5wjR46wevVqVq5ciY+PDwkJCXz44Yf07duX//f//h9BQUEUFBTQr18/9u3bx1NPPcWCBQtYvnw5wcHBxcZ1+PDhAPj6+rJy5cqSvr23BIc7B2pfUa6uMCIiZZ+yCrNSUgHs2ZnDuTOuXQGyYmUbLVqXv+Yxf9T9aceOHezfv9+ZaFy6dIk2bdpw8OBBqlWrRmhoKAAVKlQotoyYmJirdvnZvHkzc+bMcT6uXLlykf0VKlTA19eXF198kcjISKKiopz7evXqhdVqpUGDBtStW5cDBw5Qp04dXnrpJfbs2YPVauXQoUPO8/Tr189Zh6CgILKzs9mxYwdxcXHOMi8WTjv6O5GRkfj6+uLr60tISAgnT57kq6++4ttvv6VHjx7A5eQsJCQEgDVr1vCvf/2LgoIC0tPT+eGHH2jevPl1xbVQbGxssfEsK379k+/GMRW/v6Do+iJiLvqdNye1SpuWkopSzDAMOnXqVOTLP8C+ffuwXOfqxOXLXz2xMQzjmmXY7XY++eQTvvrqK1atWsWiRYucLQm/f57FYmHhwoVUrVqVpKQkHA4HDRo0KPY8DoeDihUrXtd4El9fX+fPNpuNgoICDMOgb9++JCQkFDn2p59+Yv78+XzyySdUrlyZF154gdzc3Ku+9qvFtVBxMStLCv/mOyxW1P1JRERcTn/zTUdJBfxhi4K3tGnThpdffpnDhw9Tv359Lly4wLFjx2jUqBHp6ens2rWL0NBQsrKy8PPzIzAwkKysrOsqu3PnzixatIjXX38duNz96betFdnZ2Vy4cIHIyEhat25NRESEc9/HH39M3759+emnn/jxxx9p2LAh586do1atWlitVpYvX05BQYHzPNOnT+fPf/6zs/tTUFAQtWvXZs2aNfTq1QvDMNi3bx933nnnddU9IiKCQYMGMWTIEEJCQsjMzCQ7O9vZDaxixYqcPHmSL774gnvuuQfAGZvg4OBi49qwYcPrOn+Z8NspZd3WUPH7gnWBETEX/c6bWoFre4BI6aekwot+P6aiS5cujBo1yvm4SpUqTJ8+nWHDhjm7B40cOZKGDRsyd+5cRo8eTW5uLn5+fiQmJhIeHs7s2bOJjo4mPj7+mud+/vnnGTVqFF27dsVqtTJixAhndyKArKwsnnzySfLy8jAMg1deecW5r0GDBjz00EOcPHmSSZMm4efnxxNPPMHTTz/NqlWr+NOf/uS829+lSxf27t1L9+7d8fHxoWvXriQkJDBr1iwSEhKYOXMm+fn59O7d+7qTiiZNmjBy5EgeeeQRDMPAbrczfvx42rRpQ4sWLejSpQt16tRxDhQHePTRR3nssceoVq0aH3zwQbFxNYvCxe/c2lKhnEJExHwKbyhln/duPcTjLIZhjvapY8eOFXmck5Njim4urvbCCy8QFRXF/ffff8U+u91Ofn7ZXZOgNH1mQkJCOHXq1E0/f9HMf7IypC2Rx7fyXN9wLHWvnHmrpAriH4a8X7ufWWIewNr3SZefx91KGmu5foq157g71gVDYsFiwbZgldvOcSsw42e6YOrL8P23ANgWrvbYec0Ya2+oUaNGsfu0+J2IiTncOlD7d8xx/0JERMSU1P1JbsiMGTO8XQVxAefsT26dUlazP4mImI5uIJmWWipETOjXxe/cOfuTBlWImJq+XJqU3nezUlIhYkIeWafiipxCFxoRMzDJUE0pjt5/01JSIWJiBRarG5MKXVhETEm/+yKmpKRCxISKtFR47KT6oiFiDvpdNzW9/aalpMKLateuTXR0tPO/WbNmAdCnTx9SU1NvuLw9e/awfv36YvenpqYyZsyYm64vwNq1a9m/f3+JyhDv+3VFbXcmFb8fqK0rjYgp6Ffd5PQBMCvN/uRFfn5+JCUluay8vXv3snv3biIjI6/Yl5+fT8uWLWnZsmWJzrF27VqioqJo0qRJicoRLytcUdviwTEVutCIiJR9uoFkWkoqSrmNGzcydepULl68SN26dZk+fToBAQHs2rWLsWPHkpOTg6+vL8uWLWPq1Knk5uaydetW4uPjOXDgAOnp6aSlpREcHMyjjz7KvHnzWLJkCdnZ2YwePZrdu3djsVgYPnw4PXv2LHLuCRMmsG7dOux2O506daJ79+4kJSXxzTffMHPmTBYuXEhWVhYvvfQSubm51KtXj6lTp3Ly5EleeOEFPvnkEwDS0tIYNGgQycnJ7N69m9dee43s7GyCg4OZPn06t912mzdCa2rOFbXx4OxPus6ImIO+VIqYkpIKL8rNzSU6Otr5OD4+nt69ezsfZ2RkMHPmTBITEylfvjyzZ89mwYIFDBs2jKFDhzJ37lxCQ0M5f/48/v7+vPjii+zevZvx48cDMG3aNHbv3s1HH32Ev78/KSkpzrJnzJhBhQoVnN2lzpw5U6RumZmZfPbZZ2zatAmLxcLZs2epVKkS0dHRRVbUjoqKYty4cdxzzz1MmzaNt956i9dff52LFy/y448/UrduXVavXs3999/PpUuXGD16NIsWLaJKlSqsWrWKyZMn89Zbb7ktxnJ1v04p68Z1KkTEpPRHxdSUVJqWkgpg06ZNnDx50qVlVq1alU6dOl3zmD/q/rRjxw7279/vTDQuXbpEmzZtOHjwINWqVSM0NBSAChUqFFtGTEwM/v7+V2zfvHkzc+bMcT6uXLlykf0VKlTA19eXF198kcjISKKioq4o49y5c5w9e5Z77rkHgH79+vHUU08B0KtXL9asWUN8fDyrV69m7ty5HDx4kO+//57+/fsD4HA4qFatWrF1F/dzeHT2J11oRExBv+rm5nB4uwbiJUoqSjHDMOjUqVORL/8A+/btw3KdA2zLly9fbNnXKsNut/PJJ5/w1VdfsWrVKhYtWsTy5cuvu+6xsbHExcXRvXt3LBYLDRo04LvvvqNJkyasWbPmussR9yg6+5PWqRARV9Lvurnp/TcrJRXwhy0K3tKmTRtefvllDh8+TP369blw4QLHjh2jUaNGpKens2vXLkJDQ8nKysLPz4/AwECysrKuq+zOnTuzaNEiXn/9deBy96fftlZkZ2dz4cIFIiMjad26NREREQAEBgaSnZ0NQMWKFalUqRJbtmwhLCyM5cuX06FDBwDq1auHzWZjxowZxMbGAtCwYUMyMjLYvn07bdu25dKlSxw6dIimTZu6LGZyfQqnknVv9yeNqRAxuz+6gSVlkP7Wm5aSCi/6/ZiKLl26MGrUKOfjKlWqMH36dIYNG8bFixcBGDlyJA0bNmTu3LmMHj2a3Nxc/Pz8SExMJDw8nNmzZxMdHU18fPw1z/38888zatQounbtitVqZcSIEfTo0cO5PysriyeffJK8vDwMw+CVV14BoHfv3vztb3/j3XffZcGCBcyYMcM5ULtu3bpMmzbNWUZsbCzjxo3jm2++AaBcuXLMnz+fsWPHcu7cOQoKChg8eLCSCi8y3D2rdJHZpXSlETGF37ZKGsblvwNiHur+ZFoWwzBHn4Rjx44VeZyTk1Ns1yC5OXa7nfz8fG9Xw21K02cmJCSEU6dO3fTzZ8/4H9ZVbU3zM4eYEFUbS9O7XFi7ywqGxIJPObh0OSG2dIzB+vi1k93SqKSxluunWHuOO2Nt5OXhiO8LgHX+R1isNrec51Zgxs90wWvPw8+HAbAtXO2x85ox1t5Qo0aNYvdp8TsREyo6+5Mb7yvYfdxXtoiUUsZVfxSz0JtuVkoqREzMgXtmf3I2gNrtv93o8vOISClUpPuTusKYjkN/681KSYWICTlHObi7r7PPb1oqlFSImI9+7U1IiaRZKakQMaFfZ39y0zoVhWXafjsXhL5diJiBUaR1Qr/3pqOWCtNSUiFiYg63rVPxS5k+5a7YJCJlnON3sz+Jueg9Ny0lFSImVGSgtjtPYFf3JxHTMTRQ29T0npuWkgovmjlzJl26dCEqKoro6Gh27tx5Q89PTEzk5Zdfvu7jU1JS2LZt241WU8qgIitqu/MCUCRp0ZVGxBR+2/1JA7VNSO+5WWnxOy/Zvn07ycnJrF27Fl9fXzIyMpwL3LnL119/TUBAAO3atXPreeRW4KExFZm/mTNcOYWIORT5m6JffNNRq7RpqaXCS06cOEFwcDC+vr4ABAcHU716dTZv3kxMTAyRkZGMGDGCvLw8AHbt2kVsbCxRUVH07NmTrKysIuUlJyfTq1cvMjIyOH36NEOGDKFHjx706NGDbdu2kZaWxtKlS1m4cCHR0dFs2bKFNWvW0LVrV6KionjwwQc9HgPxnqKzP7lxTIVxlW0iYh76tTcfJRWmpZYKL+ncuTPTp08nIiKCjh07EhsbS6tWrRg+fDiJiYk0bNiQv/71ryxZsoQnnniCoUOHMnfuXEJDQzl//jx+fn7Osj777DMWLFjA0qVLqVy5MsOGDWPIkCG0b9+eo0ePMmDAADZu3MjAgQMJCAjgmWeeASAyMpJ//etf3H777Zw9e9ZboRAvcI6pwOrWnALrb7o/6UIjYg6GBmqbmt5z01JSAQSeXIM977hLy8z3vZ2sqr2K3R8QEMDatWvZsmULKSkpDB06lPj4eOrUqUPDhg0B6Nu3L//4xz+IiIigWrVqhIaGAlChQgVnOSkpKaSmprJs2TLn9s2bN7N//37nMVlZWVe0bAC0bduW4cOH06tXL7p37+6S1y23FofbWip+8dsxFbrOiJiDppQ1NyUVpqWkwotsNhvh4eGEh4dzxx13sHz58qseZxgGlmJm6alTpw4//fQThw4domXLlgA4HA5Wr16Nv7//Nc8/efJkdu7cyfr164mJiWHdunUEBweX7EXJLeHXdSosbroA/FKmBmqLmE+RKWW9Vw3xEiUVpqWkAq7ZouAuBw4cwGq10qBBAwD27t1L1apV2bdvH4cPH6Z+/fqsWLGCDh060KhRI2z0jAAAIABJREFUI9LT09m1axehoaFkZWU5uz/VqlWLMWPGMHjwYObPn0/Tpk3p3LkzixcvZujQoQDs2bOHFi1aEBAQUKTF4siRI7Ru3ZrWrVuTlJTEsWPHlFSYRJHZn9xygsKkQsO2REynSPcnzQRkOkoqTMsjScWcOXPYuXMnlSpVYtq0aQAsXbqUHTt2YLfbue2223j22WcJCAjgxIkTDB8+nBo1agDQuHFjnn76aQAOHTrE7NmzuXjxIq1atWLQoEHF3sEv7XJychg9ejTnzp3DbrdTr1493nzzTXr37k1cXBwFBQW0bNmSgQMHUq5cOebOncvo0aPJzc3Fz8+PxMREZ1mNGjVi1qxZxMXFsXjxYsaNG8eoUaOIiooiPz+fsLAwJk+eTHR0NHFxcXz++ee88cYbLFy4kMOHD2MYBhEREdx5551ejIh4w+XZn9xQcGGZFo2pEDEftVTIZdfqaSFlj0eSinvvvZdu3boxe/Zs57a7776bAQMGYLPZ+Oc//8lHH33EY489BkD16tWZMmXKFeUsXLiQuLg4GjduzMSJE9m1axetWrXyxEtwubvvvpvVq1dfsb1jx46sW7fuiu2hoaF8/PHHRbb169ePfv36AdCiRQu+/PJL57558+ZdUUbDhg1JTk52Pg4LC7vZ6sst7teB2h4cUyEi5vDb7k/KKkxI77lZeaRvQvPmzQkMDCyyrWXLlthsNgCaNGlCRkbGNcvIzMzkwoULNGnSBIvFQqdOnbSQm8hNKuz2ZHhyTIVaKkRM4reL3+n33nQcmv3LrErFmIoNGzYQHh7ufHzixAlGjhyJv78//fv3p1mzZmRkZFClShXnMVWqVPnDRERErs7ZUmGxAgVuOIGSChHT0vo05qbFD03L60nFhx9+iM1mo2PHjgAEBQUxZ84cKlSowKFDh5gyZQrTpk3DuMEvJMnJyc6uPpMmTSIkJKTI/vT0dOx2r7/8Mqcsx9TX1/eKz5G32O12l9TFwEKFChXwc/HrclzI5iRgs9udKUu5cuWoXEridyNcFWv5Y4q157gz1pfycii85RdcOQhbsHnfUzN+pk9YLc5UIqRKFSw2z3wvMGOsSxuvfgP88ssv2bFjB2PHjnUO5PHx8cHHxweABg0acNttt3H8+P9n79zjo6ju/v+Z3c2NXEhCQAThUYjcFIgCgoiiElCkCI83Knh9quKtKJTap4q1lotYRS5F0WqrYPvYoFbACyoBFFv8IReBCipasQJyTwgkISG7c35/7M5k9pZskj0zm5zP+/WC7Mxu5pzM7JxzPvO97UebNm1w9OhR83ePHj1aZ6aiwsJCFBYWmttHjhwJer+6utp0vyLxwePxwOv1Ot0NaVRXV4d9j5wiLy+vSX2ptVRoOFF2HOVx/rtEVSUAwKfrcE2dCf3FOQl1/hpCU881iR2ea/uQea710lLzdUnJUWgKJ4BS8TstfLXW7yNHjkKzaa2l4rl2AiORUiQcy/e4detWLF++HL/61a+QkpJi7j9+/Dh03T8CHTx4EPv378dpp52GnJwcpKWlYdeuXRBCYN26dejfv79T3SekWWPWqZA1BNTmrIXWvTeQngGawQlRBEsaWaHzvlcPxlSoii2Winnz5mHnzp04ceIE7r77btxwww1466234PV6MX36dAC1qWN37tyJpUuXwu12w+Vy4c477zSDvO+44w4899xzOHXqFAoKCppt5idCEgVpFbVDJxJpAeGEkITDeqvrCpspVIUxFcpii6h48MEHw/ZdfvnlET87aNAgDBo0KOJ7Xbt2NetctATmz5+PZcuWwe12Q9M0PPnkkzj//PNj/v2ioiJs374dM2fOjOnz69evR1JSEgYMGNDYLpMWgmlI0DRJY74I+QnOLYSoQpCQ4I2vHNQUytJyo2oTnE2bNqG4uBjvv/8+UlJSUFJSglOnTklt89NPP0V6ejpFBQlxf7Jh1JdlESGEJCAWUUH3JwWhqlAVx2IqVOfQoUPIzc0140lyc3PRvn17fPLJJxgxYgSGDRuGKVOmoLq6GoA/BuXqq69GYWEhRo0ahfLy8qDjFRcXY/To0SgpKcHRo0dx55134qqrrsJVV12FjRs3Ys+ePXj11Vfx4osvYvjw4diwYQPefvttXH755SgsLMQ111xj+zkgziCEQEBTyKtTEXZIe9yfxLdfQvzn39LbIYTUQdCaUkLKapLYUEcoCy0VDjF06FDMnTsXQ4YMwcUXX4yrr74a5513HiZPnoyioiJ07doVkyZNwpIlS3DrrbfinnvuwaJFi1BQUIATJ04gNTXVPNbKlSvxxz/+Ea+++iqys7Nx33334c4778QFF1yAffv2Yfz48fj4449x8803Iz09HXfffTcAYNiwYfjrX/+K008/HWVlZU6dCmI3QtRaKjQXdB2If24OEfQDNhXW1t94GWiVAfek39jTICEknKCYCq4wlUMwUFtVKCoAbNn/Fxyr+k9cj5md+l84//Sbor6fnp6O999/Hxs2bMD69etxzz334P7770fnzp3RtWtXAMD111+PxYsXY8iQIWjXrh0KCgoAAJmZmeZx1q9fj23btuG1114z93/yySfYtWuX+Zny8vIwywYA9O/fH5MnT8bo0aMxcuTIuPzdpDlQKyqMrfg3ERJTYVegts/n/0cIcRBW1FYbxtKpCkWFg7jdbgwePBiDBw9Gjx498Prrr0f8nBDCrOMRSufOnfHDDz/gu+++Q9++fQEAuq5jxYoVSEtLq7P9J598Elu2bMHq1asxYsQIfPjhh3XW/iAthJBBXmpuFrMtm0SFEOAsRojDWK0TgtmflIMV1ZWFogKo06Igi2+//RYulwtdunQBAOzYsQNt27bFzp07sXv3bpx11ll48803MWjQIOTn5+PgwYPYunUrCgoKUF5ebro/nXHGGXj00Udxxx134IUXXkD37t0xdOhQvPLKK7jnnnsAAF988QXOPfdcpKenB1ksvv/+e5x//vk4//zzsWrVKvz4448UFSoghD+WwtiU4Z4QesgoolgKfDJKiLMIWirUhtdcVSgqHKKyshLTpk3D8ePH4fF4cOaZZ+L3v/89xowZg4kTJ8Ln86Fv3764+eabkZycjEWLFmHatGmoqqpCamoqioqKzGPl5+dj4cKFmDhxIl555RVMnz4dDz/8MAoLC+H1ejFw4EA8+eSTGD58OCZOnIgPPvgAM2bMwIsvvojdu3dDCIEhQ4bgnHPOcfCMEPsQIS7PMiaASCll7bJUEEIchXUq1IYxFcpCUeEQffr0wYoVK8L2X3zxxfjwww/D9hcUFOCdd94J2jdu3DiMGzcOAHDuuefio48+Mt97/vnnw47RtWtXFBcXm9sDBw5sbPdJc0YA1shpXUYUdaTid7Y8vRKcxAhxHLo/EQOOxyrBlLKEKEeIpcKO7Cx2BWoLigpCHCcopsK5bhCHYJkKZaGoIEQ1QmIq5Iz6RkpZm2cUTmCEJADWmApaKtSDA7GqUFQQohoh473UkIr69tnSMCHEVqyDCmMqFIQxFapCUUGIcoTWqZDRRIilwq6YCro/EeI8dH9RG6aUVRaKCkJUQ4SUu7NjEW5XTAUATmKEOExQ9h8Wo1QPxtSoCkUFIcohgupGyBnzHZpJaKkgJAFgnQqloaVCWSgqHOTss88O27dkyZKolbUNioqK8Mgjj0R8b8GCBXHpG2nBCBvGfBHywtbsT/KbIYTUgTWMgqJCQXjNVYWiIsG45ZZbcP311zf69//whz/EsTekRSJCYypkVNQ2YiqMHXR/IkQdLKrCjpTVJHGhqFQKiooEY86cOWbhuq1bt6KwsBCjR4/G9OnTcfnll5ufO3jwICZMmICLLroIM2bMAADMmjULVVVVGD58OO6//35H+k+aAwLW4nfClpgK+U0AoPsTIYlAkJBg9iel4XCsFBQVCcyUKVPwxBNP4O2334bb7Q56b8eOHVi0aBFWr16NFStWYN++fXj44YeRmpqKVatWYeHChQ71miQ8AhBBZSpsqFNha6A2IcRZmFJWaRhToSwepzuQCGTt/RFJJ6viesyatFQcP6NDo3+/rKwM5eXlGDBgAABg7NixKC4uNt8fMmQIsrKyAADdunXDvn370LFjx6Z1miiCHSllQ3fYlFIWtFQQ4jhWHUH3J0KUgZaKBKU+l5Tk5GTztcvlgtfrld0lQhqACP5pW6A2KCoIcRwWP1MbXn9VoaUCaJJFQRbZ2dnIyMjA5s2b0a9fPyxfvjym30tKSkJNTQ2SkpIk95A0W0IDtaV4P0U4qC2GCk5ghDiOCEr/5Fg3SCLA668SFBUOcvLkSfTr18/cvuuuu4Lef/rpp/HQQw8hLS0NgwcPRmZmZr3HnDBhAgoLC9G7d2/GVZDIhMZUyBz0jUPbVVGbExghCQBjKkgADslKQVHhIHv37q3z/e7du5txFAsXLkTfvn0BAOPGjcO4cePMzy1ZssR8/cgjj0StYUGIHwcsFbbWqeAsRoijCLq/KA0vubJQVCQwxcXFWLhwIXw+Hzp27Ih58+Y53SXSEgiZ5OWmlHVgduEihhBnsd6CgpYKpeF4rBQUFQnMmDFjMGbMGKe7QVocwZYKORW1HUopywmMEOexujxRVCgOx2SVYPYnQlQjJKZCSkXtMOj+RIg6WGMqnOsFcQoR8SVp+dRrqTh27Bi2b9+O77//HpWVlWjVqhXOPPNM9OnTB9nZ2Xb0kRAST0RoRW0Z5a5F0A/bKmoHNUoIcYQg9yfej0rD668UUUXF3r17UVRUhB07dqBLly7o2LEjsrOzcfLkSaxbtw6vvPIKzjnnHIwbNw5nnHGGnX0mhDSJUNuEhEFfhLzQbFIVtFQQkljQ/YkQZYgqKp577jmMHj0akyZNiljzwOv1YuPGjVi0aBFmzpwptZOEkDgiEBJTIUNURKpTYVdFbRuaIYRExyokKPLVQ0TdIC2cqDEVs2bNwoUXXhi1iJrH48GFF15IQdEEzj777LB9S5Ysweuvv17n7xUVFUVNG7tgwYK49I20YIQIshzITf5k8X+yq6I2JzFCnCUopSwtFerBmApVqTNQe/ny5fjmm2/g8/ns6o/y3HLLLbj++usb/ft/+MMf4tgb0jIJdn+SE6gdoU6FHdD9iRDn0Vmnghjw+qtEnYHa//rXv/Dmm29CCIFu3bqhZ8+e6NWrF84+++yoFgzSNObMmYP09HTcfffd2Lp1K6ZOnYq0tDRccMEFWLt2LdasWQMAOHjwICZMmIDvv/8eI0eOxLRp0zBr1ixUVVVh+PDh6N69Oytqk8iEuD8JGVHUxjxiexEsTmCEOA9FBSEqUqeomDZtGnRdx3fffYcvv/wSX375JVauXImqqip07doVvXr1wk9/+lO7+qocU6ZMwZNPPokBAwZg1qxZQe/t2LEDH3zwAZKTk3HJJZfg9ttvx8MPP4yXX34Zq1atcqjHpHkQMsnLKakdvKlp4ftkIMBFDCFOY3V50nk/Kg3HY6WoN6Wsy+VCfn4+8vPzMXr0aFRUVKC4uBjvvPMOvv766xYhKvS/vQixZ3dcj6l1Oguun97Z6N8vKytDeXk5BgwYAAAYO3YsiouLzfeHDBmCrKwsAEC3bt2wb98+dOzYsWmdJmogBIQ1pkLqYt/m4ne0VBDiPEG3IWMqlIaiQinqFRUnTpzAl19+iZ07d+LLL7/EsWPHcPbZZ2PMmDHo0aOHHX1UElHPjZicnGy+drlc8Hq9srtEWgpCyE/OYVbUltxOpHY5iRHiLNZ7kJYKQpShTlExZcoUnDp1Cr169UKPHj0wYsQIdOjQwa6+2UZTLAqyyM7ORkZGBjZv3ox+/fph+fLlMf1eUlISampqGPNC6sGa/UlmnQpA85YHUkLYVFGbEOIstsdSEUISgTpFRU5ODn744QccPnwYeXl5OHr0KNq0aYOUlBS7+teiOXnyJPr162du33XXXUHvP/3003jooYeQlpaGwYMHIzMzs95jTpgwAYWFhejduzcDtUlkQi0Vchrx/9AE2vwwByc6ClQesmlxwUUMIQ5DUUEC8PorRZ2i4tFHHw0K1F65ciUWLFiAtm3bokePHujZs6fp808azt69e+t8v3v37mYcxcKFC9G3b18AwLhx4zBu3Djzc0uWLDFfP/LII1FrWBDiJySmQkrtu8BBXRpcehVcKcl1/0J8W7exLUJIGMJuv0eSuPD6q0STArXfffddFBUV2dFPJSkuLsbChQvh8/nQsWNHzJs3z+kukZZAaPInKW34j6q5DHEBe55YCVbUJsR5rDEVDNRWiTB3Wo7HStHgQO0ffvgBOTk56N27N3r16mVHH5VlzJgxGDNmjNPdIC0NIUJqU8gY9QMLiYBFRLMz+xPN7YQ4i1VH8H5UG15/pag3UHvfvn1o164devbsiZEjR6JXr15o166dXf0jhMSdkIraMjWFK7DtsiuewvyPEOIU1joVgpYKpaCIUJo6RcW1116LXr16IScnx67+EEJkIwBoGlwQ0KFJmgOMQO3ADw02uj9xUiPEWRiorS6h15vXXyWiigqv14uLLrqo3gMwfSkhzQ0b3J90I6YisG1XRW1OYIQ4D+O01YWaQmlc0d6YOnUqVqxYgZKSkojvl5aWYvny5XjooYekdY4QIoFAStnam19K9Tv/j0AjmktSM5Ga5ZNRQpwlqE4F3Z+UImz85XisElEtFb/73e+wbNky/PKXv0RGRgZOP/10pKWl4eTJk9i/fz8qKysxdOhQPP7443b2t8WwZ88e3HrrrVizZo25b86cOUhPT8fdd98d8XeKioqwfft2zJw5065ukpaIAADNNB5IWYMb2Z+M1LWaXW5JnMAIcR66P6kLr7fKRBUVWVlZuOWWWzB+/Hh88803+OGHH1BRUYGMjAx07twZ+fn58HjqTR5FCEk4BIRWW1NbZkiFaanQtKgfjW+7jKkgxHFoqVCXMEMFx2OVqFcVeDwe9OzZEz179rSjPwTAddddh/POOw/r169HWVkZ5syZg4EDBwZ9pri4GPPnz8fixYvxu9/9DpmZmdi2bRsOHz6MRx55BD/5yU8ghMCMGTOwdu1aaJqGSZMmYcyYMfj1r3+Nyy67DCNGjMDPfvYztG7dGs888wxee+01/PDDDxg/fjxuuukmXHDBBdi0aRPat2+PP//5z0hLS3PojJC4Ekgp6/JHbEtSFUb6p8CmJmDLEyzOX4Q4j3UhqfOmVAvWqVCZqDEVxFm8Xi/effddPP7443jmmWeC3lu5ciWeffZZvPrqq8jNzQUAHDx4EMuWLcPixYvxxBNPAADee+897NixA6tWrcLf/vY3zJgxAwcPHsSgQYOwYcMGAMCBAwewa9cuAMBnn31mipfdu3fj1ltvxdq1a5GVlYX33nvPrj+dSMdwTbJuxZnQQG27it+xTgUhzmO5B3k7qg6/ACphm//Sc889hy1btqB169aYM2cOAKC8vBxz587F4cOH0bZtW0yePBkZGRkQQuDll1/G559/jpSUFNx7773o0qULAOCjjz7C3//+dwDANddcg0svvbTJfXtp00HsLq1q8nGsnJWTijv6nxb1/frcQa666ioAQJ8+fbB3715z//r167Ft2za89tpryMzMNPdfeeWVcLlc6NatGw4fPgzALxLGjh0Lt9uNtm3bYtCgQdi2bRsuuOACvPjii9i1axfOPvtslJWV4eDBg9i8eTOmT5+O0tJSdOrUCeeee67Zhz179jT6XJAEQwACmsX9ScKgbxzSmlLWDnQd0L02NUYIqR+6PykFVaTS2GapuPTSS/Hwww8H7Vu2bBl69+6NBQsWoHfv3li2bBkA4PPPP8eBAwewYMEC3HXXXXjppZcA+EXIG2+8gVmzZmHWrFl44403UF5ebtefEFdycnJQVlYWtO/YsWOm5SE5ORkA4Ha74fXWLpI6d+6MiooKfPfdd0G/a3weAETgphZRbu7TTz8dZWVlWLt2LQYNGoSBAwfi7bffRnp6OjIyMgAAKSkp5ufdbjd8Pl9j/1SSaAh/TIV580sMqtBchqqAPZONtwaoqJDfDiEkOrq1+B0XmUrBmAqlqddSoes6nnvuOUycOLFJ9Sh69eqFQ4cOBe3buHEjfvvb3wIAhg4dit/+9re46aabsGnTJlxyySXQNA3dunVDRUUFSktLsWPHDvTp08dc+Pbp0wdbt27FkCFDGt0vAHVaFGSRnp6Odu3a4ZNPPsHFF1+M0tJSrF27Fj/72c+wdOnSqL93xhln4NFHH8Udd9yBF154Ad27d4/62UGDBuEvf/kLrr/+ehw7dgwbNmzAo48+CgDo168fXnrpJSxduhSlpaW46667MGrUqLj/nSQR8cdUmNYDGYO+EZwZlFLWrqranMQIcRZmf1IXppRVmXotFS6XC9u3b5eSvaWsrMys1p2Tk4Pjx48DAEpKSpCXl2d+rk2bNigpKUFJSQnatGlj7s/NzY1aR6M5MH/+fMyfPx/Dhw/HDTfcgClTpuDMM8+s9/fy8/OxcOFCTJw4Ed9//33Uz40cORI9e/Y0j//II4+gXbt2AICBAwfC5/PhrLPOQu/evXHs2LGwYHDSQglzf5LRRnDchj9Q2waE+R8hxCmstyADtdUiVETy8itFTDEVo0aNwtKlS3HDDTfYkkY2kttONFETbX9xcTGKi4sBALNnzw4SKYA/sNnplLi9evUyXb6sWPe1a9cOmzdvBgBMmDABEyZMAAAUFBTgH//4BwBg4cKFQb+/e/du8/Xjjz8esZbIzTffjJtvvhmAP8OXVZycddZZWLdunbl9//33x/w3OX1OZZKSkhL2PXIKj8fT6L7UlB2B0A7D7dIAH5CSmhr3v6s6KwvHAMDt33a5XXC5NOnn7yAAIL7tNOVck4bBc20fMs91Ras0GI7JrdJSkanwNVXtO62frMBhy3Z2dmsk2fT3q3auE5GYVoDvv/8+jh07hnfffRdZWVlB7y1atKjRjbdu3RqlpaXIyclBaWmpeew2bdrgyJEj5ueOHj2KnJwc5ObmYufOneb+kpIS9OrVK+KxCwsLUVhYaG5bjwcA1dXVcLvdje47Ccfj8QTFf7Q0qqurw75HTpGXl9fovojSUvhTyfpTyp48WRX3v0s/dgyAJcOU0KH7fDacP3/2p3i205RzTRoGz7V9yDzXuiWuqbKyEtUKX1PVvtPiZGXQ9rHSY9Ay7fn7VTvXTtGhQ4eo78UkKn7+85/HrTNW+vfvj48//hhjx47Fxx9/jAEDBpj733//fVx00UX45ptv0KpVK+Tk5KCgoACvvfaaGZy9bds2jB8/XkrfCGmxBNyfXNYdsggK1JbThPjPvyG2fQbX1TfKaYAQ0jBYp0JdwjxNeP1VIiZREc0a0BDmzZuHnTt34sSJE7j77rtxww03YOzYsZg7dy7WrFmDvLw8TJkyBQBw3nnnYcuWLZg0aRKSk5Nx7733AgAyMjJw7bXX4te//jUAf5E4I2ibEBIr/iSytVYEGU0EF7/TJGZ/Ep9/CvHuUoCigpDEIOhWZ0pZtWBMhcrEJCpqamrwxhtv4J///CdOnDiBxYsXY9u2bdi/fz+uvPLKmBp68MEHI+7/zW9+E7ZP0zTccccdET9/+eWX4/LLL4+pTUJIBIQANEugthRREZpSVmJFbT4ZIyTBYEpZYsDrrxIx1alYvHgx9uzZg0mTJpmB0Z06dcKHH34otXOEEAmIgKWidoe8tgxR4ZLYTD11WQghNmN1eeJ9qRa83EoTk6Xis88+w4IFC5CammqKiuaezpUQlQmuUyGjAcNS4d/0tyVNVQS1yUmNEIex3oMUFYoR6v7E668SMVkqPB4PdD3YL/L48ePIzMyU0ilV6NSpE4YPH27+M1LDXnfdddi2bVuDj/fFF19g9erVUd/ftm2bWfyOKExgkDdufilDvvGk0lqnQtbkIsJeEEIchZYKZWGdCqWJyVIxaNAgLFy4ELfddhsAoLS0FK+88goGDx4ss28tntTUVKxatSpux9uxYwe2b9+OYcOGhb3n9XrRt29f9O3bN27tkeaKgNBqLRVCjqkCAKC5/Y3IDNSmhYKQRIOiQlnCLjevv0rEZKkYP3482rVrh1/84heorKzEpEmTkJOTg+uvv152/5Tn448/xujRo3HFFVfgrrvuQkUg//fWrVtx9dVXo7CwEKNGjcLx48fx9NNPY8WKFRg+fDiWL1+OOXPm4KGHHsKNN96IBx54AOvXr8ctt9wCAKioqMDkyZMxbNgwFBYW4t133w1r+5NPPsGIESMwbNgwTJkyBdXV1VizZg0mTpxofmb9+vW49dZbAQAfffRRxL4OHDgQTz/9NK644goMGzYM3377rezTRupC+If52ps/cgHJprURwVIhC1NUGNZUTmKEOIqgqFAXXm+ViclS4fF4cNttt+G2224z3Z6iVbImsVNVVYXhw4eb2/fffz/GjBljbpeUlGD+/PkoKipCq1at8Oyzz+KPf/wj7rvvPtxzzz1YtGgRCgoKcOLECaSlpWHq1KnYvn07Zs6cCQCYM2cOtm/fjrfeegtpaWlYv369eex58+YhMzPTdJc6FihWZu3b5MmTUVRUhK5du2LSpElYsmQJbr/9dvzqV79CZWUlWrVqhRUrVuDqq69GSUkJ5s6dG9bXyZMnA/DH4HzwwQd45ZVX8Pzzz+Ppp5+Wdl5JPQSK3tWmlJUwCRiawm2tU2FTTAUhxFkoKtQlzP2J118lYhIVixcvxjnnnIMePXqEVdRuCXyxpRLHj/niesysbDfOPb9VnZ+pz/1p8+bN2LVrlyk0ampq0K9fP/z73/9Gu3btUFBQAAB1xraMGDECaWlpYfs/+eQTPPfcc+Z2dnZ20Pv//ve/0blzZ3Tt2hUAcP3112Px4sW48847cdlll2HVqlUYNWoUVq9ejWnTpuHTTz+N2FeDkSNHAgD69OmDlStX1nleiGyCsz9Jqh7h/+GyuD/OI0mtAAAgAElEQVTJwpi0WGSLkMSAgdoKQ1GhMjGJipSUFLz99tuYN28eTj/9dPTs2RO9evVCr169WqTISBSEELjkkkuCFv8AsHPnzpgtRa1aRRY2Qog6j1HX0+vRo0dj8eLFyM7ORkFBATIyMsy+PvvssxF/JyUlBQDgdrvh88VXwJEGIgChaWa2VzkhFQFXJFvqVIS0yTmMEIehpYIQFYlJVPz0pz8F4H/6/M0332DLli1YtGgRqqqqUFRUJLWDdlCfRcEp+vXrh0ceeQS7d+/GWWedhZMnT+LHH39Efn4+Dh48iK1bt6KgoADl5eVITU1FRkYGysvLYzr20KFD8fLLL+N3v/sdAL/7k9VakZ+fjz179phtv/nmmxg0aBAAYPDgwZg6dSr++te/YvTo0WZfp02bFtZXw9JBEgkbisUZ7k9WS4W0tYVhqWBMBSEJQZCQ4P2oFGHTC6+/SsQkKqqqqvDVV19h586d2LlzJ44cOYK+ffuiV69esvvXogmNqbjsssvw8MMPm9tt2rTB3Llzcd999+HUqVMAgIceeghdu3bFokWLMG3aNFRVVSE1NRVFRUUYPHgwnn32WQwfPhz3339/nW0/8MADePjhh3H55ZfD5XJhypQpuOqqq8z3U1NT8cwzz2DixInw+Xzo27cvbr75ZgB+a0NhYSGWLl2K+fPnm32dP39+xL6SBEMICGhwBSxVUoZ801Jh7rAh+xMnL0ISAsZUqEvI9RZCyEgFQhKUmETF7bffjrZt22LkyJG455570LFjR9n9UoI9e/ZE3P/GG2+Yr4cMGYL33nsv7DMFBQV45513gvalp6dH/KzB4MGDzTTA6enppiCIxsUXXxy1avrMmTPNgHDr5yO1v2HDBvN13759g/4+4gABUaHVbkrDtFS4AHnuT6HZnwghjhIkKnhfqoUNlnCSsMQkKq677jp89dVXeOutt7BlyxYzpiI/Px8eT0yHIIQkEhrkxlQYQdMuG55R0VJBSIJBS4Wy0P1JaWJSBNdeey0AQNd1fPfdd/jss8/wxBNPQNd1vPrqq1I7SAiJM0JAwGXJ/iS/+B2AGKviNL4tPhElJAHholItQq83s/IpRUyiory83Iyn2LFjB3788Ud06dIFPXv2lN0/QkjcCaSUNStqy2kDQJCQ0GQVwDMOy8mLkMQgyP3JuW4QJ6D7k8rEJCruvvtu5Ofno2fPnrjlllvQvXt3JCcny+4bIUQGur9KhTTDgdlGbUyFf0OWK1RI9ifOYYQ4SnDyJ96QShF2uXn9VSImUfHyyy8jKSlJdl8IIbYgIDTINVWI8JgKzSVpcjEsFObihZMYIY5idUWkqFAbeqUqRUyiIikpCV988QXWrVuH0tJS5OTk4JJLLsG5554ru3+EkHhjpJQ1NuU04v8RZKmQ0hAYU0FIghEkJHhfqkXojMLrrxIxeUCsXr0a8+bNQ3Z2Ni644ALk5ORg/vz5KC4ult2/Fstjjz2GF1980dweP348pk6dam4//vjjeOGFFxp83IEDB6KkpCQufQxlzpw5eP7556Ucm9hIYMI3DRUyniSaxe8s+2T5WzH7EyGJBetUqEvo9eblV4qYpvkVK1Zg2rRpGD9+PIYPH44bb7wR06ZNw4oVK2T3r8XSv39/bNq0CYA/q1ZJSQm+/vpr8/1NmzZhwIABtvbJ6/Xa+nvx+n3SQAT8dSpkBmpHcn+SnV2WlgpCEg8uKtUiTFTwC6ASMbk/nThxAmeccUbQvg4dOqC8vFxKp1RgwIAB+O1vfwsA+Prrr9G9e3ccOnQIx44dQ1paGr799luce+65qKiowO23346ysjJ4vV489NBDuOKKK1BZWYmJEydi//790HUdDzzwAMaMGQMA+POf/4xVq1bB6/XihRdeQH5+PiorKzFt2jR89dVX8Hq9+MUvfoErrrgCRUVFWL16Naqrq1FZWYnXX389qJ/z58/HG2+8gQ4dOqBNmzbo06cPAH/tkn79+mHTpk246KKLsHTpUnz66afQNA0nTpxAYWEh/vGPf2Dfvn145JFHcPToUaSlpeGpp55Cfn4+HnzwQWRnZ+OLL75A79698dhjj9l6/pVG6BAaAhW1ZQ34kQK1ZTVlBGpz8iIkIaClghjwYY9SxCQqevTogSVLlmDChAlISUlBVVUV/u///g/dunWT3b8WS/v27eHxeLBv3z5s2rQJ/fr1w4EDB7B582ZkZWWhZ8+eSE5Ohsvlwp/+9CdkZmaipKQEo0ePxogRI7B27Vq0b9/erBNy/Phx89i5ubn44IMP8Morr+D555/H008/jfnz5+Oiiy7CM888g7KyMowaNQoXX3wxAGDz5s0oLi5GTk5OUB+3b9+OFStW4MMPP4TX68WVV15pigqjzTfffBOAvzr4qlWrMGLECCxfvhxXXXUVkpKS8NBDD2H27Nno0qULtmzZgl//+temcPnuu+9QVFQEt9st9VyTUPzZn2TWvotmqZDaFhcvhCQevC/Vgu5PShOTqLjzzjsxb9483HbbbcjIyEB5eTm6deuGBx54QHb/bGHdunU4fPhwXI/Ztm1bXHLJJXV+ZsCAAdi0aRM2bdqEu+66CwcOHMCmTZuQlZWF/v37A/D7u8+ePRsbNmyApmk4cOAADh8+jB49emD69OmYOXMmCgsLMXDgQPO4I0eOBAD06dMHK1euNP/GVatWmTER1dXV2LdvHwDgkksuCRMUALBhwwZceeWVSEtLAwAMHz486P2rr77afD1+/HgsWrQII0aMQFFREZ566ilUVFRg8+bNmDhxovm5U6dOma9/8pOfUFA4gfCP8y6pMRWGpcKyT1Z1bVNU8IkYIQkBsz8pDOtUqExMoiInJwePP/44jh49amZ/atOmjey+tXiMuIqvvvoKPXr0QIcOHfDCCy8gMzMT48aNAwD8/e9/x9GjR7Fy5UokJSVh4MCBqK6uRteuXbFy5UqsWbMGTzzxBIYOHYrJkycDAFJSUgAAbrcbPp8PgH/h+Mc//hH5+flBfdiyZQtatWoVtY9aHY7w1t8bMGAAHn74YXz66afQdR09evTAiRMnkJWVhVWrVtX7+8RGhA6huQLXVkhKKRv4GVRRW1bxO1oqCEkoWKdCXcI0Ba+/StQpKqqrq/Hmm29iz549OOuss/Df//3fLVJM1GdRkEX//v3xwgsvoHPnznC73cjJycHx48exa9cuPPXUUwD88Sx5eXlISkrCP//5T+zduxcAcODAAWRnZ+Paa69Feno6li5dWmdbQ4cOxcsvv4wZM2ZA0zR88cUX9aYEHjRoECZPnoz77rsPPp8Pq1atws033xz18zfccAPuu+8+04KVmZmJTp064e2338bo0aMhhMDOnTtxzjnnNOQ0kXhjZGaS2kjgSaWm+YPCIeS5PzGlLCEJDBeVasPrrxJ1Zn/605/+hM2bN6Njx47YsGGD6b9P4kPPnj1RUlKC888/39zXo0cPZGZmIjc3FwBwzTXXYNu2bRg5ciTeeust09Lw1Vdf4Sc/+QmGDx+OBQsW1OuK9uCDD6KmpgaFhYW4/PLL8fvf/77e/vXu3duM4bjzzjuDXKwice2116KsrAxjx4419y1cuBB/+9vfUFhYiMsuuwwffvhhve0SuYhA5Wmp2Z+MitpuDcJ4diEr/ZPxB+gUFYQkBHR/UpfQ680EGkpRp6Vi69atePLJJ5GTk4Mrr7wSjz32GP7nf/7Hrr61eNxud1AaWQCYN29e0HZubi7efvvtsN/t1KkTLr300rD9GzZsMF/37dsXb7zxBgAgLS0topAYN26c6WoViQceeCCiYDGOG9r2VVddhdatW5v7OnfujL/+9a9hnw39O4l96Kalwr/IlzrnuwBoHkDUBGeCiivh7k9CiDpd9wghEmH2J4VhSlmVqdf9yQjgzcvLQ2VlpS2dIs2PadOmYe3atViyZInTXSH1IAKDvktWMbpAK4A/pazQPIHIcElNGSpJD1nIUFQQ4gyMqVAXXm6lqVNU+Hw+fPHFF+a2rutB2wDq9csnajBjxgx4PB4WsmsOGO5PgU0hYxawpJQ13J/krfEjxVRwZiPEOUSU16TlQ0uFytQpKlq3bo1FixaZ2xkZGUHbmqZh4cKF8npHCIk7xhBvuAdJGfP1WkuFrgWGGVmWikjZnziPEZIYcFGpFqyorTR1iopnn33Wrn7YjpTc/KRF01K+MyLwRL82xEHi3+UCBAK1SFya3FgHncGhhCQEFPjqUvvUKvA94BdAJaR6VScyLpeLrjokZrxeL1xygxDsIyylrIxFfq2lAgFLhebS5Cz2Ixa/40RGiGMwUFtheL1VJqbidy2R1NRUVFVVobq6mlli4kRKSgqqq6ud7kbcEULA5XIhNTXV6a7EBWG4Jsl0fwqKqai1VEiZcFj8jpAEg6JCeQxLBVN9K4WyokLTNKSlpTndjRZFXl4ejhw54nQ3SD0Ybly1dSokLvQBGAZRzQVJmsLI/mR1f4p/O4SQGAm6/3kzKoV57WUWQiKJSgvx5yCENBSXVAtdrY+VMIYZtyT3pwh1KjiTEeIgTCmrMKH+tbz+KkFRQYhi1FbUtsH9yd+S9Q15bdGPm5AEgfeispiX27BU0P1JJSgqCFEMa3IO63Zc24h0UE2T09iRQ/6fdH8iJDGgkFAXEWKp4FdBKSgqCFGNwKBvj/uTVuv+ZN0fT05VBQ7N7E+EJAS0GipMSEwFaKlQCYoKQhQjNFBbUiNhbWga5Cww6P5ESOLCe1EtQjUFL79SUFQQohimqDC3pTRi2bAoC5lx2rovfCchxAEo8NUlRFXovP4qQVFBiKKYgdoyDh7kV6tZXktUFT7GVBCSEIioG0QVWP9LSSgqCFEMERJTIWQ+SQyqdyerorbxk09HCUkIgu5F57pBHCBs7OUXQCUoKghRjNqYCsOCIOGJkqUAkhmoLdtSQfcnQhID1oxRl7D0grz+KkFRQYhihFXUlhk8bdUrQVaL+LclmA+dkMSAxe+UpXYcpqhQEYoKQlTDdH8K2ox3IzIOGqUpxlQQkljQFVFZDFGhMf2TilBUEKIYtdZpiYF0hjXEpSEoUFvmAoN1KghJECgqlEUPsVLz+isFRQUhiiF0I6WsxOxP1vaEvx2/iJEZU2G1VHAiI8QxmP1JYYJTynIoVguKCkKUw7AiWLfiTLTc5BI1RbCokNAOISRGmP1JWUxLBWMqVMTjZOM//vgj5s6da24fOnQIN9xwAyoqKrB69WpkZWUBAG688Uacf/75AIC33noLa9asgcvlwu23346CggJH+k5IcyU0paykViyvbapTodP9iZCEgOmdFSZEVHAsVgpHRUWHDh3w1FNPAQB0XcfEiRNxwQUXYO3atRg1ahSuvvrqoM/v3bsX69evxzPPPIPS0lJMnz4d8+fPh8tFgwshsWImZpJZp0LAkvnJWlFbZp0Kuj8RkhDw/lOX0GvP74JSJMxq/F//+hfat2+Ptm3bRv3Mxo0bMXjwYCQlJaFdu3Zo3749vv32Wxt7SUgLIFRUQFKdilDrt6ZJTSnLOhWEJCBcVKqFoPuTyjhqqbDyz3/+ExdddJG5/cEHH2DdunXo0qULbrnlFmRkZKCkpARnn322+Znc3FyUlJQ40V1Cmi1GHnGXqSVkuSQZDWgh+2W0BaaUJSRRCHJ/Yv0YpQgTFc51hdhPQogKr9eLzZs3Y/z48QCAESNG4LrrrgMAFBUVYcmSJbj33nsb5KZRXFyM4uJiAMDs2bORl5cX/46TIDweD8+zTTTlXJenpQFlQHp6OoBjcLvjf91OpKWhMjCnpKakAlUANKBNTg5c2blxbeuwywUdQHpqKioC+3JzcuDOjc/fxO+1ffBc24fMc12alIxTgddut1vpa6rad7o6KxPHALjcbugAMlq1QrpNf79q5zoRSQhR8fnnn+Oss85CdnY2AJg/AWDYsGF48sknAQBt2rTB0aNHzfdKSkqQmxt5gVJYWIjCwkJz+8iRIzK6Tizk5eXxPNtEU851ZUUlAODkyZMA/KI+3tfNV1Fhvq6qqkZG4PXRkqPQvPF9cqkHLBQV5eXmvpKSo9Di1Ay/1/bBc20fMs+171R17WsJ40tzQrXvtH6szP8zkDijvKIcJ236+1U7107RoUOHqO8lRExFqOtTaWmp+fqzzz5Dp06dAAD9+/fH+vXrUVNTg0OHDmH//v3Iz8+3vb+ENGeEkVJWk1ynIiRQW1qZCkSKqSCEOAazP6lLqPsT/Z+UwnFLRXV1NbZv34677rrL3PeXv/wF33//PTRNQ9u2bc33OnXqhAsvvBBTpkyBy+XCz372M2Z+IqSBhI35khoJc6nVNEnZnyIVv4t/M4QQQurBiKExJoBoNYtIi8RxUZGSkoI///nPQft+/vOfR/38Nddcg2uuuUZ2twhpuYTUqZDzIDEop2zIfkkIi6WCT0cJcQ5aKgizPykJH/MTohjGGO+S6f5kSSkLYSl+JzOlrI8pZQlJDCgqlMW0GMs0hZNEhaKCEMUwUspKdX8KajDwU4PcBUbQ01F5zRBC6oH3H2FMhZJQVBCiGCJgObDNUhH8hpy2AFoqCEkU6P6kLnpITAWvv1JQVBCiHEb2J5lNiPBNWRW1Y2ifEGInLH6nLqyorTIUFYQohun+ZG5LqnIdFAgu0RRuZn9ioDYhCQdvRbUw3V0ZU6EiFBWEKIbh/qSF1JGIbyPhu6TPMTqfiBKSEASJeqoKpQhNKcsHPEpBUUGIagQGeQ0yYyqiiAhZVhEgpE4FJzJCEgLeimoRWgiJY7FSUFQQohhGRW2XFrwdX/RwjydZ2Z+MQ1qKLAn6cRPiHLRUEKaUVRKKCkIUI9zlVYb7U+hCQpPTjr8x/w/GVBCSGAjLkwTei2qhh1oq+IBHJSgqCFENPaSitqx2zEnF2AbrVBCiEnxYrSChosK5nhD7oaggRDGsa3xAYpiDFrzp35bp/mSxVDBomxAHYZ0KZTEsEy6JGf9IwkJRQYhiGClka92fJKZ5NQ8v86lVhEBtTmSEOId5+9H9STnMYZiWChWhqCBENczsT4FNWTEV1sMalgu73J90zmSEOIaZAQjgqlI1jGsfWF4ypkIpKCoIUYzajH82WA/M9rRAexKtIkGTFycyQpyD8U3KYtapMLYd6wlxAIoKQhQjLKWsJmfU1yIVqpA5wVjdn2ipICQBYKS2eoRYKqgqlIKighBFke7+FPkNCW0FfrL4HSGJQVCZCt6LShGWCcSpjhAnoKggRDFqA7Uluj9ZYypEIKBCWkwFK2oTklgwpkJZBC0VKkNRQYhiGGO+S2pyjgg+1Zomd36xxlQwOJAQ52DNGHWpDdozdjjWFWI/FBWEqIYtKWUR4k5twwQTZJ3gREaIcwRVvHSyI8RuzEDtwJjP+DaloKggRDGCsj1CokdSaKC2LPcnQfcnQhKKIE3Be1EprJZpohwUFYQohpH9qTY7k4zBP9j9QdhtqeA6hhDnCCp+52RHiO2ExlRQVCoFRQUhimEM8VLD6IQeplVklakwD2qNo9AZU0GIc9AVUV1CYiooKpSCooIQ1dCDYypkD/nCag6X4v4U+KlzIUNIYsDsT6oi9BBRweuvFBQVhCiGaamQ6fMqrClrrVHbMt2fWPyOkISA7k+EokJJKCoIUYywQG05rYSHasiO2wuygtD9iZCEgO4vihGS/YmXXykoKghRjpCUsrKK3wW91ORll4yU/YmWCkKcI6xWAVEG0/2JgdoqQlFBiGLUzvf+CV9m7HQD3ohDm0wpS0hiYM3ExntRKcKqq/L6qwRFBSGKIYzid3IbCfGv0vz/ZE4wOhcyhCQErFWgMLRSqQxFBSGKIy+molZVmLpCakpZigpCEgPei8pC9yeloaggRDFMS4UGaELIj6MTfiuFJju9ZJCoYKA2IY4RFFPBRaVaMKWsylBUEKIYZkVtSHSBCnJ/khzrYBwyqJ34N0MIaQS8F9WiNme5o90gzkBRQYhiCGEd7GVZKqxWg4B80SDXFB50bK5kCHEeWirUwwyoCWzy+qsERQUhiiECT/Q1c50v4YmSiBSnJ+vJlRFTwZSyhCQEocVwiDqI0JgK57pC7IeighDl0AL/a5CWVFaIEJ9auy0VjKkgxDGCKmpzVakUYSllORarBEUFIYphjamw4zGSCLKGS4ypCEopG/9mCCGxQkuFstBSoTQUFYQoRmixW3kpZS2bchsLHNtycJ1PxwhxHloq1IPZn1SGooIQxbCKCn8YpYyYipDidwEPKDkLDNNUEWEfIcR2hOHySFOFchgWYxfrVKgIRQUhimEsvWtjKiQQUohOaEZrNlXUZqA2IYkBF5VqEWoKJ0pBUUGIaliK38lLKQvUphS0bNrl/kRLBSHOYVgqubBUEFbUVhmKCkIUw7BUuDSJ63zD3SlsgxW1CVELLiqVhIJSSSgqCFGM2hTymrSETICwxOlJLn5nVtSm+xMhCYWmUVOoRlj2J34BVMLjdAcIIfZiDPFmGnEZD5QiuSJpsjLBGMXvOHkRkhDQr15dwupUcFyuC6H7gL3/AVqlA+XHgZOVQGqa/812p0NLz3S2gw2EooIQxbA6BmkQ8p4kBnk82bG4CA4OJ4Q4BeOblCVMUPL618m/NkNfOCPiW1rhGGjjfmZzh5oGRQUhqhH6IElWSlmjvF5Q8TuJCIoKQhIKuj+pB4vfNQhRfhwAoI28DlqXboAnGaL0CMSShcDJcod713AoKghRDDOlrCYzyau1ToUlUFvmYj/o2AzUJsQxbHuSQBKPkDoVVBV1U1MDANCGjYbWOsf/GoDvnb81y1PHQG1CFMMWd+ew7E/Gtk2igpqCEIfR5N/zJPHQw0zhpC58Xv9PT1Lwfpcb0JvfREZRQYhiWJ8hyi4d4W9Q8xe/k+0KwZSyhCQGVksFF5WKEWql4hegTrx+SwU8IY5DLheg++zvTxOhqCBEMWrdn/yB2nIyyoa6P0V4LaPN2g157RBCYoPZn9TFxZSyMeENWCrckURF83s4lhAxFffddx9SU1Phcrngdrsxe/ZslJeXY+7cuTh8+DDatm2LyZMnIyMjA0IIvPzyy/j888+RkpKCe++9F126dHH6TyCk2WBm/JPq7yyMKhh+dCHFFUJEExLNbywmpOVguD9qtthCSSLBdMINw1vjP1dud/B+lxuCoqLxPPbYY8jKyjK3ly1bht69e2Ps2LFYtmwZli1bhptuugmff/45Dhw4gAULFuCbb77BSy+9hFmzZjnYc0KaF6ZxWpMYRmmNqTheBogfghuPWzvRrCDNbzAmpOVAIaEs5qVnnYqY8HoBt8fvHmyF7k/xZePGjRg6dCgAYOjQodi4cSMAYNOmTbjkkkugaRq6deuGiooKlJaWOtlVQpoVYcXvpLYCiLJSi8iIu6qI+JITGSEJAFPKKoj/goctkklkvDVAUlL4fro/NY2ZM2cCAIYPH47CwkKUlZUhJ8efXisnJwfHj/tz+ZaUlCAvL8/8vTZt2qCkpMT8LCGkbnTLJO93TpA0+BuH1TRjmon/Yl9E2dC5kiHEMegCoy7GQtiMqWh+C2NbCVgqwnC5m6WlIiFExfTp05Gbm4uysjLMmDEDHTp0iPpZEWFREkkRFxcXo7i4GAAwe/bsICFC5ODxeHiebaIp59oV8N1sk5vrd4HSEPfrVuLxANX+10lJyaalIiszCylxbEvU1OBQhP0ZGeloFad2+L22D55r+5B5rg+73dChwe32wIf4jy/NCdW+08dTU3ESQFp6OioBpCSnINumv785nuvjHjeqk5PD+l2SnAzN7UZOM/t7EkJU5ObmAgBat26NAQMG4Ntvv0Xr1q1RWlqKnJwclJaWmvEWbdq0wZEjR8zfPXr0aEQrRWFhIQoLC81t6+8QOeTl5fE820RTzrXX63/6cexYKSAEdBH/+8N36hRSAmK/xutFqgDgAo6XlUKLY1siUDgIQJCpuLy8HJVxaoffa/vgubYPmedaD+Te9+k+AELpa6rad9p38iQA4GTgZ3V1lW1/f3M813p5OYTLHdZvny6A6uqE/HvqevDveExFVVWV+eWrqqrC9u3b0blzZ/Tv3x8ff/wxAODjjz/GgAEDAAD9+/fHunXrIITArl270KpVK7o+EdIARGCxL71OhWZ9EXCy8sXbFB4lULsZ+qIS0mKwZoMgamG6vhnuT851pVng84bXqACabaC245aKsrIyPP300wAAn8+HIUOGoKCgAF27dsXcuXOxZs0a5OXlYcqUKQCA8847D1u2bMGkSZOQnJyMe++918nuE9LsMFPKaprEgrfWwA3NdH8Suh7fCI6oMRqcyQhxFM38jyiFMcGwTkUsiJqa8GragP/8eWvC9yc4jouK0047DU899VTY/szMTPzmN78J269pGu644w47ukZIi8QsfiezEWF5SGmtrhvvJy/RMj4xUJsQ57BaKrioVBNaqWLDV1egdrX9/Wkijrs/EUKcwayoLWPsty4kfKI2pWzc3ZKiFL/jQoYQB2H2J2XRQ689x+I6qSulrK/5uT9RVBCiGLVjvn/QF1JUBcxJRYjAwChDVFjTFUZLL0sIsRljkHG2F8QJQkQFh+K68dZEtlS43c0yHS9FBSGKYQRquyDT7VnUHlcXtamgZbo/sU4FIYmBAACtNliXqIMIiamgqqgbb5RAbY2WCkJIMyC0LpUUT6HQTEzC8lpWOyKKKxQhhBB7MONpGKgdE946ArWbYRZDigpCFMPIv+SPqZCfUlYYA6MmIVA7Wu85kRHiIIypUBfLeE/qJ4qlQqP7EyGkOSACE74GTZ7LszAis+F3RTKyQcXdUhGteYoKQhzDuP25sFSP2pzlwdskMt4aaJEsFXR/IoQ0B4zAbJcGQJM04Fs0RZCQiPcgGRSozexPhCQGwZYKinyFCC1+R1fUuqmz+B0tFYSQBMeaQt5S6zrurZhH1XX/RKMh/ubcqPMVJzJCHMcM3Gp+izlsOQMAACAASURBVCPSSBhT0TCiFb9zU1QQQpoBemCel55F3GqpEPAvMOJuzmVMBSEJh5H9yQyscrAvxBno+RYbPm+UQG03RQUhJPEx3J80TXJMRST3J5nZn2LZTwixgdAUc81vcUQaSaj7E4fiuolWp8LlkpDYRD4UFYQohmmdDtmOP0agtmGpgI3Zn+LcDCGk4bCosoKwTkWD8EazVND9iRDSDDCGeJfMlLJBlgphZpyyK/sTn4wS4iBm9icuLJXDlkJILQOh+/xzYsRA7ebp/hThLyGEtGQEQt2fJDu/yrRURBUPnMgIcY7ADW8sLFnhXh1CRQWJjteLU550/OA7G+nfn4LPJ6D7AE+yBpd2BnLc6WjldB8bCEUFIYqhA9DMp0eyJvtaS4VR/E7TNBstFfFthhDSCDT6P6lHiPsTLRXR8XpxOK8PdlV3BTZUhrx5Ic48owK9HelY46GoIEQx/J5JRgE8We5PsDyl1Gsb8dkUqM0no4Q4R5gLTPNz4yCNxPSvZaB2vXhroLv88RQDh6YjM8sNlxs4VS3w0coT8LqSHe5gw2FMBSGKIbTaG1+aqLAe1eer3Yz74iJa77mIIfYjdn0BceK4091IIJhSVjlC61Tw4kfH64WuuQEArbPdSGvlQkqKC5lZbqTipKQaUnKhqCBEMQQ001JhbEtoJLj4nbEj7hW1G7ifEEkIIaDP/Q3Euved7kpioIGWCiUJHXw5GEfFWwPh8jsMaSGrcU0TgKY1u2r0FBWEKIYRMy29lbDid5AQU8FAbZIg6Lo/PeSpaqd74jwiJFCbt6M6GItguj/Vj88LXfOLCpcreFbWIPxWjGaWAYqighDF0EVt0Tt57k8ISilr7oj3E0sWvyOJgpHZrJktAqRCS4V6mOE0FJT1UlMD3WWIiuC3/Kev+RXAo6ggRDH8MdSBQG3NH2MhpRFDVfh85oNL+HwQu7+B/senILasl9Cw0T5nMmIzPoqKMLiwVJDQlLK8+FHxeU1REZqBV4OAkJExUTIUFYQohjVQ27/D7w8uKk5AVMfJdcNa/M4I1A64P4nP1kFs/AT6otnQ/7EKoilxFrRUkESBlooQNC4sVURYLNP+HU71JPGpqYHQ3HBpotayE8D/wM8d/zhEyVBUEKIYQtQGamtuNyAE9F/fCf3BCRB/XRSvVmpf+Xy127oP4shBoN3pQNceEIv/AP2JX0Ic2NfIgDROWCRBMNIl09Wn9qGCEX3Kc6IQoemEnetJwhOwVBieA1b8lgpXs7t3KCoIUQxds8RUpGdCdOoCrUt3AIDYvyd+DVktFQjMMboOHDkAnN4Jrl/MgHbbA8CPP0B/9B6Id4oa3ISIVo+imQ3EpAVAS4WFxgdqi8oK6MUrILxeKT0jkjEDtQPX/j/fQv/TMxDHSpzrU6Li80HXPHBFcEH2WypczW48oaggRDH8KWUDaBqQdxpcd/0S6DcYqK6KVyMWUeE1YyyE7gMOH4SWdxq0pGS4LhoG12/mAb0KIIqXN9z9KloQG92fiN3otFSYGPd/IwK19dkPQRS9BPznWyldI3YRuPandYDY+A+IlW84251ERPdBd3ngckWwVBiigu5PhJBERggRVFHbQEvPAsrjVbjLIl2sMRXHjwHVJ4G802rbbX8GXKNuACorGh68zToVJFGgpaIWoftXRQ20VIhDPwKGtZTirHliVlP3Ly+1YaOBc8+H2Pr/ml3NBenoPghXFEsFaKkghDQDBLTaitqaZb7PyAQqTsRn4LcGaltdlA7+6G/XIioAAF16+H8ePdTAdqIMuJy8iN0Enig2KfFAS0EIf47MhloqKsprXzezxRQJEOr+JADtvAuBkiPAd187169ExKdD19xhhe8Ai6WimYlrigpCFCO0+J25/k7P9E/kJyskNBoQGSfK/NuhosLt9o+ivgb6UUddeFBUEHsRxnd3/15nO5II6Lr/SXVAVESNfYr0ewYUZ80bS+Yv7byBQEYm9L+9SNFtQZjuT+HvaZpg9idCSOKjaxpcQe5PgQk/I8v/s/xE0xuxKhcRwXCQ2TpoU9M0wO3xVyRuUDtMKUsSBG9g8q8+6Ww/EoFQS0WMBbyE1VLRzJ7QkgBhKWUBrVUGtGtvA77/Bti9y5FuJSQ+L4TmgSu0SAUMLwK6PymD+HIbfM886g88JaQZIayDvWUs0zIy/S/iElchwl9bG/N4wn/F0xhRQfcnkiB4a/w/m9kiQApCBGIqAkuMWOfJfd/XvuZ5bJ6IkPE+sK2d3sm/fbLSgU4lKD7dn1I2oqVCCxS/a15rzAgzO4kF/eX5QOkR4OhhoG17p7tDSMwIWJ8maMHuTwBQEQdLBUSt64NAuM+VJyn8VzwewFfTwGa48CAJgvHdpaCNEFMR4+8lp9a+pqho3oSICqQEru2pOGUYbAnoPuhaKlzuKJYKzd3s7gNaKhqLISQCgaeENBcENLPYjgZroLbf/UnEy/0p5HXQsOmO8DzDndRwS0XUOhVc2BGbqTFERfNaBEihke5PQTFVzewJLQkQaqkwCIiKBqcNb8n4fNBd7igxFbFnfxL/2gRRViqhgw2HoqKRaAFRIQ7uc7gniYUoK4XY/Y3T3SB1EFSnwooRU1ERp7SyodHgZsU9zR+YHYrHU+tCEiuMqSCJgrEgbmZPFuUgAJc7/Gl1fViEhKCbTDMlmqhI8f+kpaIWI6VshJyymssQFXWLa3HiOPQFv4M+9daEEGwUFY0lPcP/8wAzfVjRX3wa+qxfQMTFhYbIQEfw+t6c79Na+UeyeFgqEJT8I+D+FNjh9vgDs0PxNMJSEfWpMEUFsRkv3Z9MhAjK/hS7pcLyudKj8e8XkU9InQpzOzkgKhJg4Zsw6IGK2lHdn2KwVFjS9OovPgXR0AdzcYaiorEYOckPOGup0P/veejvvZ44RWVO+QcMseFjhztCoiG0YEuFmavD5fKL5XgIQhHB/8kgKUI8BQB4PLVpOWMl2oCbKPcDUYcaWipMhADcLph+HbGmlLXc/6KhDxhIYhH0VAkWUZF4lgpRXQX9/16w34XIDNSOJCq0mESF+O4rwO2Gdv3twLbPoM/7LcTe7yV1uH4YqN1YjCcqP/wb4tCP0Np1cKQbYu17/helR4Eb/gdaUrIj/TDQOnSC2L0L4oO/Q3TvDa3jfznaHxKONVA7KKYC8Pu9xutJUmiQpjFuRoqnMPYzpSxprhgLYn73/Fjdn2K1VFg/18zy85MAEYrfAYDmcgNJyeaDx4Tiu68h1r4LsX0j3LNfsq/d+iwVcAH6qToPIf79FXDGWXCN+G/omdkQf3kO+uOTgPxe0Hr2hXbJFUBWa//5twFaKhqLMYFoLui/uQ/6q886Y3Zq5XfDEh+9B33WVIgdnztrtTAmAp8P+qyp0D/5EIJP7hIGIURwoHboWJaUDNTUPYjF2FJInQrLdzJS5iegcSllowZqN+wwhDQZU1SoPd6ZadZdrlo3xxgtFcJnOXc6LRXNEvNSR3BxTU5JzJgK4949egj64j9AVJbX/fm4teuDcLkjiwqXBuGKIfvTjz9A69wFAOC68DK4nvwTtLE3ATWnIN5+Dfovb4M+7R7oH74FYUNhTloqGovPB+TmwfXLJyA+eAvio/cgvtgC7fKfQLt0JLSU1PqPEQ90H7TCq6H1KoC+eCH0eY9Bu+AS4KJCaL0K7OmDFZ8POK0jXFNnQn/xaYglCyHWfQBt0KXQ+g+B1jrH/j6RWoSAHuT+pAU/WU1KgoiHqAhdQ1hTytbh/tTgitrR1AOfFhO78dJSAaD2PLjcFr/6GIWWj5aK5k/kOhUA/MHaiRhTcSow5/XsC7F+NcTOz+G67QFoPfvKbdeoqB3VUhFDnYqaU0BKWu3vZWRBG3UDMOoGiP98C/H1FxCffADx+ssQbywGzsyHdt4gaAMuBtq0ixzf2AQoKhqLz+sPOM07DdqEuyHOKYC+5l2IN16GWP5XuKb8Dlp+Lxv64fP70/XuD9cTL0K89zrEO3+D+GwdXM/8BVpmlvw+WBA+r78/2blwTZ0JseEjiLdehfjbixBL/1TrV0maxCFNi26RSs+E69G50Iy6E1YMS0VgM8z9KTkl/pYKk9pA7Yi4PQ03jUd9iqP4wo7YDwO1/Riiwm3N/tQIUUELd/MkLKWs5X5ITk1I9yfjQZpr/ETgZCX0l56B/syjwPkXwnX5aKBLNzmu5T7d7/4UMftTbDEVxhowEtp/5UP7r3yI4WOAshKIj1ZC7NwK8fclEH9f4k/O0qsAWnYb/wPxdqc3+U+iqGgsuh50IbWCQXAXDILYvhH6H6ZD7P2PTaLCay7StKQkaGPGQ3Q6C/qiJ4D9e4DMc+T3Iag/tV9wTdOgDboMYuClwMF9EJ+urX0iQJpEWloqTp4MNyOLw/uBbZ/5CzNGExWaFuT3GLQE8iTFSVSEtmuZY6K6PyUBlRUNPC4DtUliILyBBXGsQcktFUNcBcVUxHhOBC0VzZ/Q7E+Wt5JTIBIwUNsUOkkp0NqfAddv5kOsWgbx/pvQt3wK7dKroE24O/7t1pVSVtNiK37n9fqt/HWgaRqQ3cbvFjX2JohD+yH+tQnYvQvi319BbNsI8c1OuB+d25S/BgBFRaMRPq9/0Azl7MAi3gY1LnQ9TNwAAP6rq//9A3uhdbNZVHhrwp5Ea5oGtD8D2n/fbG9fWjCZeXmoPnIkbL/YthH6ts+ixyaYMRX+TS3UVJGcAhw/1vQOCr+KMNf21jaiDYCNqVMRdbGi+MKO2I/puqf4E3bT/cmS/Uk0IqUsi981byJ51aSkJKSlwnzYGfCk0FJSoP1kHMTQkdAfug2iNHyujQuG+1Ok4ncx1KkQus//YC2a9T8KWrvToQ0bXduNP8+F2LWjQceIBgO1G0s0k1OyjQVejAE49AuVkwckJwM2BOVE7FM9qplIxDj3UQOe63F/Skpq+MI+cjMhgsXiDhXFUqF5khoeUxHVUtGwwxDSZJj9yY9xHtyehlsqGFPR/AmtU2EdjFMS0/0JNYE+JQe7OGmZWUDXnoCkwG3h80HX3LWnytq2pvnPYV2WCnMN2MTMTilpQPXJph0jAEVFY7G4HVnR3G7/ws6OYCRj8A5ZxGsuF3BaRwgnCvNFOS/EJozvQrTFeSBQO5r7k+aJV8o/a2R2yGY00cmUsqQ5YzxRVP27FymmItb4COvnaKlonpgpxCMEaienJmSdilr3pwhxE63SG+6WGyPCpwOaK2pMha65gjOihRJlDdhg4phKnqKisdQRHGNbMFIdKlVrf4Yz1b7rOi9EPu56LBUB9ydj5PdbE6yDfjJQE6fUyNECteOZUjaqqGjYYQhpMsz+5MewdLpd4VWV64OB2i2A0EDtWrTklAQVFacAT5L/gWwImkxREbDgRXZ/MiwVdYjraN4qDSUl1Z+CNg5CnqKisdS1eE6xSY2bZuYIi7Tctv6CeHbj89FS4ST1uT8ZgdpGTEXo+0lxDtQWZrP1Wyoak1I2alYZxRd2xH7o/uTHjKmwuj+x+J0yhGZ/st4OiRpTUXMqzPXJpFWGNPcn3Rd4uBdFVAi46s6c5rW4GjYFowRCHKwVFBWNpS43n2SbbhyrmTmUpGTA57W/8FwgpSxxiBjcn6wxFdC0kJiKOKWUFZFSygaarCv7U4MDtZn9iSQIXro/AQB8gXvY4659BBvrPCTo/tRiaE4xFaeqo6e7b5UOVFdBNNSKHgO6aamIkv3J5YbwxuD+1NQ1V6ohKpoeV0FR0VgiZV0ySEmBsMX9qQ6Vai4ubR6Y6f7kLAGrlahLVGghgdrWNVDAUhGXquzWcVIIvzkXiO7+xJgK0pzRaakAEPL0lHUqlMP4/rsixVT4H7jGZX6JJ6dORY6nAPyWCgA4GX8XKKH7z1FU9yegbnEdL0tFckBUVDXdw4aiorFESykL+G8cW9yf6oj8N6oWxyOTT0PweaHR/ck5Ysj+pKPW/SmMpGT/JBCXpzJaZCekOlPKqiUqamoEvtx+0jSDk2aMYu5P+h+fgtiyPvwNa50KVwNjKoICtSkqmiXmpY4wySSn+r8LCVavStRnqQCkxFUYSdEiWioCVbb1uu4dYw3YxEBtzbBUxCFrqaOrvyNHjuDZZ5/FsWPHoGkaCgsLcdVVV2Hp0qVYvXo1srL81aBvvPFGnH/++QCAt956C2vWrIHL5cLtt9+OgoICZzpfX6C2BFUbsQ8AtEhfKI9TooKWCkcxA7WjXPeApcIgPKVs4GlNzalaYdoYAnUqICL41dYlKoQOofugRRPskdqJ/EasPXWUXVuP47vvBNKSfTizR4bT3SFNwczS0jy+e01BCAGx8ROIjZ/A/eKK4PcCiR40TyNSyjKmogVgxFSEbAP+mArA726UEmUR7wQ10UWF1irD/xdIiKswhoyIlgrj3qnrgVPA1bDJD3JT0vw/42CpcFRUuN1u3HzzzejSpQtOnjyJ//3f/0WfPn0AAKNGjcLVV18d9Pm9e/di/fr1eOaZZ1BaWorp06dj/vz5cEW6IrKp64l8SgpQVmJDH4wsG3W4P0nwA6yTBEwpK4SovUFbOp7AYryemAqXxf8paF2ebBEVSG9aX4LcnyyvowkGI+GA1wskxygqmntMxZ7dAM5EzZc7gR4XON0b0hRMS4Wz3bCFuhb8Xsu8pDXQ/YkpZVsAdVTUTrE+Ec+ys1N1U1OX+5NES0Xg6x45UNsFQEDUKSriY6kwBVUcPGwcdX/KyclBly5dAABpaWno2LEjSkqiL8Y3btyIwYMHIykpCe3atUP79u3x7bff2tXdYHy+qIsjza5A7brcn5yyVMRQMt5OSo548c7SMpQcsVlcOYV1YR4JgXqK31lFRRMQQeHgtWiI/FgGaKQQblpKWfH5/4MoOdyA9uKLO80/mHvLjjvWBxInbKpTISorIA7tl9pGvVhERah/vBlP6HbX+tXHbKmwiIpYhQhJLIxrHaFOhWaIiqr4FFqLG9XVdWd/AiAkWCpEDO5PdSbbqStZT0NIDVgq4uD+lDAxFYcOHcLu3buRn58PAPjggw8wdepUPPfccygv91/MkpIStGnTxvyd3NzcOkWIVBIqpWyiuT8ljqg4esh/jg7us/k8OEW92Z/0oOJ34Sll4yQqQg8uLCbx+kRFQ9LKNsFSIYSA/tws6L/6WeztxRm3299PX3niTLJ60UsQX25zuhsJhRACetGfIPb9J/qHbHJ/EksWQn9kIkS5g0LUeo+G9sOYczye2kewjUkpy5iK5okhOCN5BxiL10QTFTWn6oipCLilSrFU1B+oLeoS5HWtARtC4G8Xzd39yaCqqgpz5szBbbfdhlatWmHEiBG47rrrAABFRUVYsmQJ7r333gZlDCguLkZxcTEAYPbs2cjLy4trnw8LgZT0dGRFOO6J1tk4WXMq7m2Gcmp/OkoBtG7TBskhbVXl5KIMQHZGBpIk98PA4/EAug9pGRnItKnN+jiUfQxAFTyeFOnXw048Hk/Ev0d4vTgEoFVyMjIivO/zuCCgwR34/eTkA6iBzzxWVZs2/u9NeqsmfW8Ou4JlS+2tqyEtPT3i96MyOwcnAORmZsIdY9uV6Rk4EWF/ckoScuo5hvD5zxUA5HhccGfnRvxctHMdD35M8Q/Bvmr540UsCCFwqHgFRPEKtHvzHxGLQckk9Fyf2r4J1f+fvfeOkyS76ny/94ZJV961754209PjNK7HSjNyg5BAaJGAXWCx0iJYzD4kYAXL8tnHSiyLW97iVhg94QQ8gUBC3sxIM6Pp0Xjfdtqb6u7yJm1E3Pv+uBFpKiOzXGZWj1S/z2emuioyM25G3Lj3nPP7nXOePkDXD/4kYg012MHUBONf+RT6K59i5J8fjZVTTlmSEoCmrfdyMjuHB6QOfIWuH3zvij9nNfNazTpE/F5vfg535+7ysazrMg+kunsRUpAFursypJZwrknLIgr/2NJisMl7tO9Teulp3Bv3I67gPL52rh9XIsYFBMDA4CDjQCaTJhN+/9LGzUwBPa5Nog3XZKXXejzwcbq66Y3bU7u7uQxkUOXv0SpMhc5Af38vQ0PpmmNj3QDTpJLJhs9B8UIX08TbgMuBcizGgC7HIr3K77jmToXv+/ze7/0e9957L3feeScAfX195eNvfvOb+a3f+i0ABgcHmZioNHSbnJxkYCDeELj//vu5//77y7+Pj4+3dNzKK1HwfUoxn6sCjS4UGBsba6uWX4cszcz8PGLBOHTeRAKmx8cQ3f1tG0M1hoaGwPfJl0oUW3y9V4pi0UTc5+byLZ8Da4mhoaHY7xM53rnZWQpxx6cn0UKgA5/x8XE8z8PzgvJn6byRLkxfvozoGax7/1KhVGCilJEzET0HAvKFYuz8UHkTJZkcu4xYIomq5uKjtaVicdH7ravYxPHf/lXkD/8MYnhj3esaXetWoJSfB0YIrASX//cHEd/3biOfXCPoqm7qY7//64jv/iFEb2fWD6i/1urLn0Z//cvkXn4O+TO/iuju7dhYqqHHL5X/PfaH/wPxb/49IlObWB+Uo6+6rWuNGhgGIPuPf0lucgLxrh9Z0ZxZzbzWM1Plf09/7p8RvcNlpy+YngEgXyqWpTBzM7Nkl3CuoKr5ll9q/gzr559E/dEH4TW3I//jr8QXLLkC0M7140pEEMrfJqemAcjOZ8lH+0u4H89eulhns7QCK73WQSGP0k3sRNsmOz5W/h6tQjHsbTM7N4sznqs5ls2b37PzWXSD8+rQHp6Zq7cBl4NIsjg/MUFuCZ+zefPmhsfWVP6ktebDH/4wW7Zs4e1vf3v571NTlQXriSeeYNu2bQDs37+fAwcO4Hkely9fZnR0tCyX6jhU45wKEgmjB2239Ggp8ievc7IfrfUVl6hdKYb0rZA9GVaMsO1KEn8dNJpaZro9ORXUyp+iiHercyoalpRdwnuj53P7Ljj2Muq//hTqi/+CbmVH8cUQGl3+wCb0Q19A/eZ/Rr/49NrVca9as/Q3vor67/8X6mufQ69RJR6dz0IqDWdOoD7wHtTHP7I2Y4nW0e5e9Fc/i/rl96CefARdvcZH42r3rQt86B1AvOnt6Ac+jfrQ+9FHXuzsnIn2nlQG/ehXUL/0o+jnHjd/88Pnx7arnvWVJGo3f4+eN84LLzyJ+uDPo4++vLRzrKO9qJM/Vc3LpInG6ytN/lRqIn8CU9GzDXmyutz8rv5YOaciWELzu0a9n5YKxzX3qwXN79bU+jty5AgPP/ww27dv55d+6ZcAUz720Ucf5dSpUwghGB4e5r3vNRTvtm3buPvuu3n/+9+PlJL3vOc9a1P5CRYpKVtVNq1RRYFWjQEaJGqvQJ++Wqgm41krhOtZp4tgrSksB3wfffww+sIZxN1vrHSx1qCFrEnUroHbwpyK6k+vKm+RFTbdca+2bXO7GjpEMWiYzLkEAyucFOLeb0fcdAfq7/8U/U8fRX/+nxBv/3eIm+6IZS5aiaijqtq6G/nT/wX1D3+O+oNfh517DXOybWdbz1+H8L6LH/xJxN4bUH/zx+iPfRj90BcQb/0e5J2v7+x48jnYtA35wz+N/tKn0F/+FPrAg4j73oJ4x7/vXHQ6vC7yh38Ghjeg/uqP0H/2O+juXuSP/zzixtuqjOA2G/dBAKkU8gfei77pdtRH/wD1u78KN+5Hvuk74fpb21/tLipn/v3/ATG0AfWPH0X98W/Ajj2VSI7lVPJMmhlG1VhOn4qsET6Kd78P/cm/Rf3Or8DNdyLve6u5H+tYGzTLqUhFORW5+mNricVsNcdt0Z5Yi2j7irtUItwzdbN8pGY24DIghAhzgVfvOK2pU7Fv3z4+/vGP1/096kkRh3e96128613vauewloZmEfmowkGxAJk486k1KLeNt2K81LVI1PajCX7lMBVRjtO3ClMBgG2jX3wK/eBnzKJz6HnEe43TjlY1ze/EwpKyTqsYLm3K1UaORbhAPjN4DR+av5m/Lvj0JhfMk8UqV8WeZhXN76oSSkX/IPKnfhkOPof6zD+g/7+/QD/0BawP/snSx7ICRJGqIBCIm+9E3nAr+sCD6H/5a9Q/fRTrff+9reevQ/maOIgtO5Af+C14+lHUpz6G/sj/Qu+5DjE43Lnx5LKQ6UJs3Yl498+jb7sHdeAB9Oc/Adt2IW6/tzPjiAwKx0Fs3Yn8z79pIuSf/Bjqb/8E67c+0rESqLpq7xHX3YL8jQ+jv/o59D99FPXiU8hf/A245sb2DqKKJRd7b0D+wofQD38B/bXPw9hFc8x2qtaRpZZjW0b1p2wWpETc9QbErXejP/ePhjV57nHE978X+ea3N3//OtqDslMR/l69FkeJ2vkrh6nQKjDzuSlT4baHqQgvUlwQIKoIpb3G64r2m7QVWC4SqZYwFVdM9adXHQK1OFPRAq+v+RialBNbC/lTs/GsEaJ9yfe/tZwKLp43hvz1t6BPH68c04TN70T1nypwwioQ3irn7sLLHToVx7uNlPHcTEzUZyXyp4bVn5bwXq9iQJshSsQNtxpD+tqbOtLAMnIqIn9c2A7yvm9H7H8dnDjSPErVDvgLrokQiP2vQ/6n/2Z6nHzjq50dTz6HSFX6pYib7kD+0M+YX2anOzeOslNhopnCcRG3vRZx690wPWHuUzQX2y1D8msDWsJNIL/9ncgP/E/zhzZUqalDxFSEa71IppBveSfiu36g8hopV1dSdjGmIjcH6YyZo4kk8p0/jPyffwHDG9FHX1zqN1lHq6HC9TumT4WQlrGPriSmIuru3aikLIDjtkUWq5s2vwtfU2piw7WqTwUY2f6rvU/FqxVaKTMbGvWpqO4a2U40o77CiLPupPzJb1F5sxYi0hkH30pORXT9U2nE4IbaTqBa1TS/C2MhleMtYyoWRF/CVdPWZs6Ozsc5FRV27fhkgd98+Bxnpoucm232HK2io3ZoQIsFncOFEIiNWzqimaswFQsO7N5nyi5eONP2MdTACyVhC6/JE8FD3wAAIABJREFU8EbYewP6yUc6O558ttJ8KsJalKWMnoeFEonefmP8zs12rgN0I+ltyIp3JCeoQT6f2Lyt8ouUVYblEuVPejnyp/lKuc/o/LZjrkMnSrqvIx7BQk3PgrU4lTayxisFkZ22mPyp1PrnKprhsc3vIqeimdqklYHcRLKmeMlKse5UrASL6djcKvlTW8fRJEknWuw7yVS0koprEaKgofctJn8CTEQok4HcfCWJU+uaHOo61tWJHOLVLqA6PElt9oYMl9FL8zHzsoqp+MKxKb5xdp6f++xJfv6zpxqfpmFKxRLud7Pnx3Y6Ih2MhrnQHhW7rzXHXznc9jHUoJxkW39NxIbNZR17x5DPGSOkehyOY9aYFlD1S8YCpqI8lqgM8cxkjREc/N5/RUcyoFajkfS2k5LXRnvgxgVORRSCXTJTUbU6LeKI6Nx8vLy4RdrwdawQ0braKK8nmb6y+lQ0eLZr4LYpp0JH8qf6Y+UWL82CW4GPBo6csJmaWGUQLJFaZyrWDGVjpIHxXE7U7pBTcaV01L6C5U+dJGzWHJHBkUiaSJ5SNYtFIG2mixkKeQWI+JwKvwXVn2J+HVABP26NMDbdxKkIfI5NVMbrKd24sk0jw2MpToXnEUgXLRsYaJ1wKhoxFUMbTET+4rm2j6EGCyRhNbDsjlY80J5nNvJUpv5gMtVRw0RX5VTUoDdyKqZqcyoOv4A+9Up7BhME8XtPuXJbB52KBWx9TS8RIarCrUtkcbQyPoUQizsi2fl6FgvM/tvuvXcdjRE9BzEdtQFIptBXElMR7Y0RAxoHJ9GenIpmHbVFlFPRZM31fTyni2PHBV//yjz53CoaRrZI/nTlhJRfTQgfGi0aGM/R5OwUUxEXtYo2vw5a0/oKlD9V70ta6/ZXRVkEpaJCWgLbbuM47AVOBRgJVDIFWuFbSYrK5eVn8yZRu/q9oWGi/+kvCb7xNRMlTmUQA8OIN34HYvP2JQ4inqkYQDAnJHIq5vuH82auGHBqqsgPvmYIRwr+6rkxCr4m5cS8Z6myijj4Hl9801+w6UKO/QvzWm0bgsBIHduIck6Fr2vmpxDC3ItOBgUAfI/p7p10ywR1cTvb7qx3HuW0xBmOHXYq8ErMdm2jx3KpWfXDHh56erJerrOcKmbLQeAbA2AhytLFTsifllB1ppqpWGqeiTKBDpP8tVhOxTxiZFPdn0UiiV5nKtYOZflTg3ufSl9ZORWhnebZaRqmarsuzE41OrpiVBK1649V5E/NmIqAXLJSOOPRB+a45c4MgyMrsMESKZiaWPx1i+DKsf5eTQgCRkfu4MXp13LvXEBX94KFNaz+pAuF+pKdLR4HEL+wr4H86cpkKioLWuCvvpzzavHAZ2ZRGr7ze/sWf/FKEX1JN4HIdBmnITcPA8OhB2GWsrFLPizoHSksC/Hm70JPTRjnOZ+DqXH04RfQj3wR9lwHjovoH4SNW43ha0koFhEDw7BxizH2fG/BSmn+bQXGEOwu2vVOXugMvXR6HE0/1114gfM6DfQxPz5OatNI/XddRfWnqNHb6HQaFWikVT2WzjjlkdpDK3O5a/zxDrEl1dAljwN3/jrOQcVbr1tw0LI7u57kcxTcXvJimLoer8lUR2vde0XF1+/6DQafh3vurzrQGy9/Mm9qn1OhY+VPnWQqfJ686f1smOxlYdFj8R9+Af0Xv2eMyuj5XqpzrsNghBZLyqnw0v31hmCLIq7rWD601uWgaz6v4430ZBrG2yQNXAmKBebTm3j46G6uSRTYe12y7iXCcdHtyKloJn+KnIpmTEXgk08Zp+LmO1IceanAga/Os3mbw1VXJxgYspYcSBUtyqlYdypWgsBnqu9qfG3z+ENZ7nx9ptaxCBu8tD2SFnqwJ05ouvo8NmyuspidzsufoqZU53KDzD6TY99rUu2NyC9lTFW25fxcQN/A2k75KOhw6cKC+9VKRE5dNVORDaO+YUlZAXgljfJ1nf0tv/8n6j5Sz82i//5P0WdPgldCHz9cVx2pzoyXVR5LOA1cZebjEA5/++A4P/SmIYQQXJov0ZvI4AjB06cmSY/k2fuJP2Bq6Aa4/oeY++s/ZfgDv1b/XVdRaUdVleo79GKBfTcmsSLHYiWVqFYAXSUhv3DWY+tVTmUTsDsrNwIIwg3MCyTnz5TYvK1qPI5jDNpOMX65LIev/n4uXNrLzSdLtdemw0xFZE9MTMDMVEBvf1j1yAkTg2emYpiKNt27IOBQz32Mf36W219Xtfc4USCp/UyF9gPGhm9m7Bykznts3FJZy8S2nWYtkNbKmQqhm75HK8Wks5lv2N/D3pfy7L0+WZkbbnJd/rRWqFqvHvriPG+JeYm4AuVPhYRhHI+8WKCrW7J52wKetl05FRFT0TRRu7n8KRc6FZu2umza5vLKoQInjha5cNYjnZFs2eGwYbND38AiDkYi+epvfveqRRBgBSFl5mke+uIcO69OsOfaBK4rIRklard50wt88slBDr5YAowBsO81STJdVlUr6c5Wfyq4vTx/eStcLnFp1OfaG5NsqjZMOoyIqLAdeOxr81x9XZKdVycqxmOH4TgCz9M8+fUsV1+XYM+1ydaPJaphv1D+BFUlZQ3GL/sUXIXv66YOoOjuqfS6CKEvng/zh7T5efEc+tIoIplCPfIlsKYp15cKIzJuby8AxYSib9zhwNfnOWRn+fSJad60q5ef+/U/4plH5rm5W+B+6E/oOjcLhyA3H18mUzfSXS+Fqah6Nk4cKXLpvMcNt6UY2eh0LCdJKw0Sevstnnsix/kzNjfeljLPsO00r/zRBvilimH8zGM5Th61uOn2NN294ZqitTH8OsFG5rMoae7Dc0/kOHPC4pobUwyN2GYD7KBh4nuV6/Lwl+bYssPhmhvCtba3Hz09VS/XadO900HAqeRNMKt46Itz7LrarCOOG96jTuQCVTlMT349y+btDte+JkU6I8vOlZAC3UgC0/CDq2STzeRPhTz55CAAR18uMjkecMMtKTNP1xO11w5VhndkMM9O+8jpgK4eaXIHUh2WLi4CXcijqvLqnj6QY3Sbx94bknT3RA57AlZbZj3u3OG+KGPso0o6UvPmd7n0CI4rsEN58L4bU+zZl2T0vMe5UyWOHSpy7GCRRFIwsslhw2abkU1Ovd3xzdD87lWLIEALCykU972ljyMv5jl+uMiZ4yWuutpl+y6XhGW1/8EJAsaGXgPApq0Oly54jJ7z2LHbZe/1SewObTCV8fic2Xo/As2N+9OcPFbk6cdy9B62uPamJEMjdsedi2hfuu8t3bz0TJ5Dzxc49UqJa25IsmWbUyt56QQEbNlhDKWjLxc5c6LE1dcl2bbTbZ1zUV39KdSj69x8uXysDpmKu96Q4esHZpjJBXz1c7Psvd6MIy5pLParbNxS+4c91yH2GM2M/9wTKKbww+uf9TR9gCOMcXH3GzL82ecuc8uFLvq1y+2yi0dPzDKVTzPpCfbv3YgY6aNL9sOh02RLCu37MR2UV+5URFH56/eU6N7Sz4tP53n8oSyDwxa7/CEGhYVss5REa7D9HPfev4mTr5Q48mKer31hjt3XJLjKzeB0mKmInIqb9nnorh4Ov1jg4S/NsW2nyy7ZRQrCPgmdcCpyaCHJJAN2Xd/F0ZcLPPbVeYY22OzN7KBv6pn2jyFEpEC47Z4005MBJ48VuXDGY/sul939W0nOTtQzFW26d9PuZgCuuynJ7HTAK4eLnD5RYu/1SbYm0tgdkD+p8MG+Znseevo5drDA6DmP7Ttd9vb4OLBC+VOUU7HIe7JzeLbJXdx7fYKTx0o89CUT3Nvm9JMJ/AbrxTraCr+EL11sVWLHHhe+BqNnSrzyxTmGNtjcdHuaZDIN2TmTh5RIQiKJkFH3aAUXTptg2NlTsHOPMXS7eyCRQgiBHj0HXd2m3PambTA1btQhs9P4pZ3oy5dM3sb4JdixG5FMNx0yxUI5eHHvt3VxedTn2KECF856bNhss3tfkr62lZRtxlSEO7bfzKnwyKeGyXTVfoDtCLZd5bLtKpdSUXF51OfSqMfouRJnT5ZwXMHQiM3gsM3AsEVPr2WC4V4JrQLTT2SFWH/iVoIgQEkbiSadkdxyV4bd+wIOv5Tn2MEirxwqcufQDQx2IFF7bPAmkmnBbfekKRY0R18ucPp4idFzHm9wU9idlD/5PkW3G8dW7NidYPtOl3OnPY68lOcbX8uS6Zbc9fouE83q1JjCyFemy+LO+7oYu+hx8Lk8zz2e49DzgptuT7dPhhQDFWgSScn1N6fYttPjyIsFXnw6z8ljRe57S3drHIsoyp5MQSaSP4VMhe+jhXEq0hlJ/6DFnAxIpSUvPJVnbibghlsXWYSXgJcv57hzROOFEd5CaISkwhVnc69LfjDgE+Pj/LuhIW6a7iKF5OHRWb5zbx/3XWUYjYxrFresnTLNzgYWqOtXI38KF2tpWwxvdHj9W21OHy9x/HCBJ/P7GLj1A9zT5pwKrUEKhZCCXXsTbNrq8PJzZh3JbfhObso+2NbzL4QfXhM7Idm8O8HGLQ6HXyxw9mSJUX07bxQfQfpefKJwi6Fz8/hWGjchuGpPgm1XuZx6pcgrh4sc6P0e7rp4gU719vZ981xmuow0YtfeBMcOmrV2auhdvO74H8c4Fe1Ze8e6rkZoxbZdLq4r2XWNz8HnC7z8bJ4jd/w2r/UfpKctZ64genYsW7A7DEYcO1jgzMkS2XyWO6AmUbth9baF0Dp0RJrLn8hl8W2zTl19bZKr9iQ4+FyeE0eLnND3sfeqS+wtFVvTFGwdS4fnUXJ7sQtjjGwyEqJN2xysG5IcfbnAg5+d5d70EF2A+qUfq7wvdC7QGuZm4j/bcY2z0KTp5cI0Y/GuH0G87Xubj7lUcSpsW7D3+iQ7dpu15uSxEpcenGeftY9d3qdaLv1cWqL2IkxFapi+JjaVm5Bsvcpl61UuSmnGL/ucP11i4rLP6DmzRtkOXGXtYy8YJy61chtg/YlbCQIfLSyEqCx6PX0Wd7yui+lJn0e+PM9c73YG213hwPeZ6bmKwWHDACRTgtfsT9M/aPPcEzmyXVvp7WSkM7wuMrwuQgq27XTZvN3h+OEiR14qMDcTdNipqDR1BRje6HDft9uMXfR5+kCW0XNtzG2IgVIVmfHwBoehEZtXDhU5/GKBYl6R7mpBBNiqYiqSabM6RfKnsVEUKUCEjITAsuC1b+7i0QfmmZluTQOvgqeQQmBZkgLQkzTXWGoVVpoUvO+ezcwWA64ZSvH0gSz25TTvfdsI3YnKspRxzcXK2UlTfaPOqVh5SdlosRa2OYdlGcN+x26Xpz53lpnCSPvlT1ogqr5DKi3Zf0+Gpx7NMnVyO8x0mKmIKsqG9yCRlNx0e5qRTTZPPQpTfXsZaVdVo4XI5wjsAWzHPBOWLdi9L8mmbS4PfGaWOWeoY06FFwiQRr4IkExJbrwtjWUJTh4eQPtezX0E2sZUFO0MLkUjtQV6+23uen2G86c9nn0cssVU+52KIFrjzRhSaclr9qfp7bc4/UBUUnQFze+UqpI/NXmGS0U8O4MUpppewhLccleGa29SfP0zY8z07DR5FXGVw9bRPngexUQP6cIY3X1mDenptei7Pklvv8UTj2QpXr2f7m9/JwxuMJKiQr7yn+fBrmsgO4vYehX60gXo6oH5WZidMQ7H1h0mR3DjFrg8an6Wioj+IboImMvmYH4O/Q9/Zo4vhkKBIHQqIuVCIim55oYUu/clefiLc0zlh8x89P36stKrwFL6VDSXP5mSsm5iaY6OlIKRjY6R+AK5rGJyzOeVQwVGsxtCpyK/7lR0HCpACYs4lUgmTJoL3Ay6uPryXE0RBCjp1GnhoyTC+a7N9HY0Uds3DM6C62JZgo1bHI68VEAttQlSq8ak6h9YIYy20EnIxpr8tozFJEVXsxFCCDLdZvVouQ0S0cqpTNmp0KPn0Owtl4KPSsoap1QyN9sap8ILTPQ9aUvmACf8zkIF5cVyU7fLprB31cgmmwtnPVROUF0yJOOYF2ftlEmGXYiG92/x+1rNVFTDsgQpx2datj9RWmkQMWPtG7AYPddPSVk0qZ7eckRf107Ubg3DGx0kistDNzPSqUBFIY9vJUkmau9PtIH6OqaCWJvgKwmSsm45QjIlUMLGUzbuAqaiXZ2tNbJuzgghyuu+as0j3HwM4XeVC/ae/kGbM1FHSikrEZ3lVH8qN79r8gx7JXwnjWPVviaZkiRsn0C663kVawG/RMkxi7qbkmGxQXOPkilzX1VXP/J7f3xJH7fcJzs1NER2fByA4LEH0ZPji7+pWEA5Jg92oarTtk2w1itEldWKLXMqtI6kyCp2DSszFUo3liT5Psq2VyzjTmck6YzLxJjPpWy45hdWp7BZb363EgQBWto1TEWEyD4J3HRH+lQoYZUj3xEifV02vbGzORVeyODE6QOj7pAd2PCqoXS8XhHMntfmNgS1Y1lQvjuCFW7Mgd8iByeS7EQSlUwX5MJE59GzaCkR1BdmcROCUnH1YwiUxgv0goqy5RWybr4CDG0wC/XEWK3B6lgSVxqnQsc5FY1aai/ha6iwy7p06hdraQlDibedqaA+wg30DpgxzdgxZXTbiGgO2snajdO2BQPJecYHr+/cmuKXCKxknSFvNn6NLzvXx8MPzKStcyrS5u8Fma43gi9daMtYlJZIUT9nIpvD78CaFuVUyAVWWFdP1dhkNVOxTPmTWKSkrOfh2Wkcu/5zLQsCa72s7JrA81AiLBRSNoLNPYqCaS3b5xbDwJDJt1gMxTzKNaGbuHxCx5V4UdeeVuZVaGXspQabVXnLFLJxmeggiLUBlwvLFgQ6/JBVVk5bdypWgsiYj6WsBFJCYHegwkGU27FgIJYtSGUk86kNne2AGzEVMbMq+lsnjXgwXn6jSGbnnQpdPm81IqbJD1rlVISyHjeFXShA7wD6zAm0CtAXz6OFcSqEFDWRIDch8EpNulcvEXPFIFwoBQub3wkdxN6PRDK8Bl79uTOuRTaSPy1Ew/u3BKYiuk5xToUUKGG1vea/uUoxTkUYdZ5xNrT1/AsRyXftRP01STk+np3p3JpSKuHbyTomVgiBLQICK9mxKjKesrFUsW6tTSbNw1yUXfWLSRs68ALh81s/v6OxdWJN02GDM2HVLmZSCrq6RPRLhalYiVPR7D1+Cd9ONWj8LoxTsV5WtvPwSpWKXwsVC1HwrFX73CIQA8MwOb74flYoEDihUxFDBriuoKTbUK45UMaObOhUhInaQjZcS7TvoxvYXMuBbRs2VsM6U7EmCAK0sBtGwC1bmEm6ypuzKPzGzEBXtySbHOlsScrARwk7tjxaZcPrsPxJE6tXBPPQdnI80WZvLTBMolzCluUEBz5Ii+GuYUYOH2Pw277PMBQf/3/h9CvocAUq7/fh21xXoLUpk7waTBfMFzHzstapQMUzFVIKEPHzI+NaZJPd8fKn1TS/86MIWoxTYUuU5TavEd4CaB1vILquxNZFilZnNeF+YO6TE9O9XEqB7oAkrAzPI7ASsaWObWk6w3fMqcDGVvUbeyTpKFiZjpWUVVTy1qoRTeNOOBUqiORP9Q9zKuodJsTycyqW6FRor2SYiph5apyKdfnTmsCrbnpau8FE87PNtS8q6B8y+QH5+HLkEXSpgIqcipi9yXEFngo36VYyFSowAYKYZxmqmQqrsVMRPYdLrNjYCMbhC9n5VTJ8607FShAEKBnPVEBIv9qtaSTSDDoqbRszkK5uSc4Z7CxT4ftoGe/krB1TEb9QgPn7KoPyy0KUb7UwGhJFcOKi9Cs9UdcNd+JKm8C2cVLdcMtd6Ac+DVDFVNTu3W7CXKjVSqBmiiEDENNRG6WXLUfLuBbZRFes/KlY54Qs8JSaoGwYxc3XMALbbqfcFNCMH6ylg3LH1U4hUGHSeowhbyRhdsesAuV5KOnEjsWWGt/unFPhY+OoeoMikQqZCrunfjFpw9qrVbT3xDAVkbxEtX/OlBO1rfqHZ3XyJ1VxKpo9xJ6HZ2dinQrbluvyp7VCDVMR3ZsF8qcOMRXloh6L5VWEORXSWrhnGTiuQGlpkrlb2asiCIy9tJj8CdnQmQma7GHLQRS4aQXDt56ovRIEgYnIN2MqZKLtG170cMaNw3YEvnTXgKmId3LWiqlQWjdkKqTVWScnSgoXdUxFixfbwMfu6UehKfR0k5ybw/rp/4KengDLQX/2IkIJs3dXvS1KgC0VNXSv/PQzhQARXvdyo73yTQhimSwInYqYa9DlSqbdDEzWlxL82GQ3767+Q9kWWYL8Kaw0s/B+QMVAq+663Q5oLZpsKhrV4biPryRSlGKfYSFNUnK7JWHlsURJ47HRaDoqf/JxsKnf2C1L4FCikOjrTJ+KMJAUN2ci+17p9s+ZcoTUrmf5rMjhqSopu6xE7aW8J0rUTsQ4NY5xKnSpsOxE33WsDtrzjBFMaKBXRa2iYFon5U8aTF7F1qsav7BQIMgk6xQEERzX/N2z0zgtZyqsxkxFNLWFaMxUlPM0V8lURPlYVhJdWN1zs85UrARB44g8mI3G0K9tZipUk0irNA900xrHrR5PpO+L0SWuGVOhGz9wUopYI7ZdqMifav8eRXD8liVqBwg3gRICbUlEpH/uG0R094TVnsyiLypWeK1TsQrMFHxEuQJ3BPOb1EETpkLEzo8tPS6n7T6ys/N1x06VGlTiWIb8Ke75EaGxFLTbqYCGm4pErYlTYQfxG2enmYroeYhrNWDb4Hciby2Eh4ut452ppCxRSPRXqMgI7bhO5by1+jkjpEDogKAD7FaZqYhxKsoOj6hyKpadU9GcRtYlk6gdl/tju9Z69ae1gu9VAklAddhKCFO+vGPyp0FTcFqPX2r+umIeZSdi7RYwsmAAz+lqbU6FClBCNlzhy30jm8ifGuVpLhdR4MYwfKtbU9edipWgLDuKP2zbmEWtZLoTtguqzFTEMQO1r+kIQqZCxJQ3K+8tayB/ashUdDhRu8wsLbg+VhtyKqTjooVASwupVM0GravZG1GVU1F2KlZ3UaYLQTkRvLyplJlw3VyOFnPq127vwRcWTzhb8ANVTrw7N7s6o0E1c8pDrbjy2p1TIRom6kmhy1G/TsHXVmzuAITXRMi2X5PyWML8jlj5kyNMTkWHknF9mcAR8U5FwvIouT31D3BbnIqQJW8kvSXoDFOhayUt1ZDVJWXLGo7lOBULdJkxUJ4JYDkxToWVsE0+VLtzGtdRD69Uv+FW3UbLFp2TP/UOmD4l5083f12xiLLchmVZy0yFk2mx/Ek1ZyrKLL9seF7VRK2yHJSlaVZi1c74uvxpBdAqQAm3oYzDsgWeCCOohfY14GlUohSq5BuddCp8P7YaFYQGZoNE3HZiYfO72jHR0T4VqmqvrYYMK4a1lqlIoqVAhbSIUAodcpxVleANTxHlVITNtEql1TMVu8vfsWphJAAVX5MbIqai/tx7h5IMSY8/3Ps9/OE/HEUAd23rwlcgw8FH3ykQEksHSzJimjrlkVPR5vqcpk55M6aiBc0QlwFf21i6MVMBtD15PUIU+I9N1HakoeqLYx2RuPgygU18gMiyoCidjuRUlJmKZuxWB5wKVe5vF7PWh2PTK0nUNh8QvqfxMxwp8OKdCgco4pf8uqdnOu8zmffZNZCse986WoBQ/qRrgkmV+9hJpkIIAZt3oM+faf7CYgFluXUKgggV+VMG7XmtW2/K8qf4wzUlZRdhKlYrfypXoLRT60zFmiBobDyDcSr8sFZzO5PFdBPqay3kRuXE8QYef6eZAQhzKhpFxoVo3DutHWNp4FRAGMFplVMhBNJxUVKWHYlIAgWRoyXwlWam4FMKjWvLNmNbrfzp7EyJpCNrEjaiTxQ6aBhVEQ3mhxSC922a4wdOfoEf2Ony9mv6eW40x5Pn57kvbSRRKlyBc9Jd8jib3Q8Zam5Um+WDmsbVP6TQKGGtusTvchBgN5T5RD0JglJnJJXl3hBxToVrhcUwOiNx8WQSR8R/bykxVVMWoh0sdZOKf2CYiqAD23rTvSdM1NasRv7UPFE7Issig68adtgwMyjVW69/+exlfu2BMx19pr6l4IdMRTlqVcs4WVYHmQpAbNkOF043v9/FPIHlNLRb3GqmopU5FcHSqj8pIdEN1jndKqYiUksku9aZiqVC/fnvQjJt3ORS0bQ+z2cR6S60Voh0V+UBkNJodXv7IdNtoiypjAmdeSX0gQfRV/0UokEQ0bIgCGMk6iP/C3H1daa9vBM2I5ueMOdyXXQ2i+jphew8JJPmd9etrZyRSKLnphFdPZUOkTNTqLF52LaI/GnsMsH/+U0TNbNNOUiRTKEvj0IiadrYb9pqvm9XT7kcKaUiYngjeuJyeN6xSmfmRNJci3QG5mcRQxvRM5MUvCJq5N2xFUGiMXWUOSGSP10ZTk5F/xhXsaR1TIX8qQ8gT5/HT6ZQUflYFaAwho8GNJr3/MsrTBeM4fOH3xjlP9y2wZTPWwVT4QWa45MFehIWJgs6+q4hU6HVipoRXr8hw7WnH0QOvhlxzS6+74ZBnjo/z+ueCDuFR59v+pOWGYxmqER5YsbiSEChO8BUNNoPpAj1tIFPbEH+NsAXNq7OxY8nYm8W5g60ayzarKGxvQgSVsfkT1prE8lsMDmlFPFORTuuU9SbqNGaJjrDbimloUrdVDOGUP6ktMQqdz1dTknZxR0R39dgxTucZSlHsd6pODSWZ76kGM/5DGc680x9s0AffA594AHYewPilruMoaOUkRf1DcLYKBTy4VpczYVX0FH5E8CWHfDQF2BqAgaGjHOhFMKyTOES11QJU9Kp66YdwYkYfKcLSgX0xXOQ6YGpMcMglErGdioWIJUy1aYGhs3v6S7IzhrFilcCy0LPTCFSafTJYyi5Y9GcCoREf/lTqPFL5nyWBYGCRAJ18QJc1bpLAbMbAAAgAElEQVTqT76VQn/lU6iBIWM8lYrmeZyZgp4+mJ81v7/v1xp/1uqG8uqBPnnU1CuWlrkojmOM9Qtnzc9CLlzMtLn5TgKycw1rjau9SWSDdu22LQiEYyzFVw6ij7wIiVSF90tnjGHueZBMoY++aAz2fBYy3UZeEFamKTs46bCk5uCwudFDG1CbB4EmidqA2roLRs+Z7+t5YNno7BwMjcDstKmKMDlmHoqpcbODB4GZ/E8+AoMj6HOnYGSTeV1vv3lYpGU6xqYz6BeehP5B1PwsekdXuRxn3JjWIlG7sYa/w30qGpSUhYipaM15xMatiIl5tGXFMxUY499NCK4fSXFyqsgDx2c4NVXkbVZ/neOntKboa/zICBdgS4ElDIsgRcVxOzFVwFOaHjdiKhbqa1VDhq/p/ejtN2+fmUIAvUmbN+/uI3g8NGCESQ7R4fdTsKhpVWEq4uRPNlAql51tFzSisZRFavyoq3ennAoc0sRPxIokrDNORVQaNTanItTNq0Kx7XH5coGFBhOqnMDe6I2tROCH+XyN2C3TUKvd0KFTEfvshAyDQmCV94KlrrO6Eulu8hYVOhVx96QcdS3WztPpgs/FebOfn5kurjsVy4R+7Kvoxx+Cxx9C/80fN37h1jdTu+6vjfwJQGzfbfaDD72v0q/CD4M0vlcOJCvZmKmIll7P6UL//Z+1hOWKPkHf/P7YHFSo7Km6qxcOvYI+/UrFRg07zqvunUCr+lRA0D8CZ0F//CO1L3BcYxfbocxz3akA63/82bJer7U2F29myjgHXsk4AokUSIn+wnxsOT0IKT4tkX/yCWOgF3KGJdGmKj2WrGphv3KIKR++NN8gpyJ8zU/+ClZfZ3TZQ0NDqI8cb2I0rk2fioaaxQaJwe1Cc6ZCtC6nApBKoS1ZaXRXJcVQWqMFvHf/Rh49M8tY1uPn7trIbz1ygYu2x9FsgT+4MIqnNEVfleVRDc8loDdhMZC2iQL73WWnIkQ4SaNSs7Gf02x+9A6Ynwt7VYQLfCT5MJVHBPPapr/pqBeRP7kh0+i1d4IoZFNNrepkszkgEA6WaORUdOaaRPDD7SkuGu2EEhe/GLR9E4sqgMU1eoPQqRAdkj9FTEUj6a3QBMJBBwGikRfUAjR7dkTZqaiiMpYavKmJAjVJ1G5Q9AKqoq5Vldsm8z6/+dC58u9nZorctqVraWNaBwBfUcN8+u4PsHNjH28RF+jFwyHAGRrBnryM09WNffxl9DmLqACZFvDypRz6YpabNmaMNNzTaK3xFXhK4QeaktIESuMpbeKpYeBKa3j83ByHxvIMpW3u2NrN7oFkWDVcl4NI5YJj2RKTeR+tNZYQ9O3eh/jxn4eXn4W+ATO3EmG5/54+yGahf5CgNIDbSJYrBLYDav/rEXs1DG2EfA4xtAGSKXBc9ORlhOOis/OI/kH09CSiuxddyCG6eiGZNN5usWACyMWCUcG8kETY8fk95X3hbf8W+ZPvMqyKZYWKmaIJEmcdeMRvyP4vFVGFPfWGdyB/9O3mPJYFtmvYYCdhVCqp9KJG3LeMU7FclGss9w/GHtcqXjYBYQ31cF8Wtm1YgDagaaR1rfpCNG0211lmAKKciivDySnfr4ZMRYuujdaIsEGjasBUaODa4RQHzs4CcM/2Hn72TsXMCwrXFtw2ksGxBAlLkrQlri1wojmlIdBm8Q+0phSY3IzJMAny7m3duHPx9LegcU6FtJrMj7DYgf74R1COg+jpM5/9ysHwc8O8ECnQAnJYizoVzXThUYS13fNVC9E4p0JGTkXnes0YpyL+oRC2iZJ1jKlo4lREJRD9Npf8BQhyJnFRuvHbpWUJlBUnf2rD4uJHvYCa5OFYYRS2jU5FFKyNDWiF8ietqSx2S86poCpDtfHLImVZLFMRyZ/CuaG15g8eG+XoRIGMK5FCcGS8wMuXc9hS0Je0GM44DSVlEZTWvHAxx96hJM9eyNKfstnem6ArJln8mw2B0vxD4lomrAyXZgUPBdsqBycBemEcsDbym+ohtBb8/oEL/EcFR8fy/N2DZ9nU7XJLsYtEIPndvzu/ZO4KYHO3y/MXs3z2aH2vomb4mTs38pZ73gT3vKnp69QXZmMrmUWQUqD6hpH3/2jsccH14U9ifzaCPjqPbLDHVD8GIlPlAMtQaQPoogf4LcipCJ8Z6SJ6Fjg5dnju3sV21PDlqxvKty6U0k0TtcEEqqw2XuGmkdZwnWtjRdtY6EWM+LUoKRunTDDj6bT8qXlORT5eyr58aFN7Q1sSHVV/WqDvFhBuhqK833/bnj6+fmoOyxb827vinemlIjhIVcIl5Z+iWeK8bGw/CyFgeCOMXUR/7MN1G1LSsaDokXYtgpJgnsWlDVG36tjmd5ED1Xb5U5NEbQladJipkHalednC8Tg24HWsYlpUuyduDS03jCy1f0FR2RyQwHLj55S0pWkKWPfGdjAVPlomEFb8nLAkeNKpMOttQrOAlohyKqqZiiUv/EtL1G5Unhuq5E8ho/bCpRzPjmb5kZuH+bbdvfzuoxd47Owcj52dK79nc7fLu28d4bYtGXyl+ceXJsh6ivfcOsKXj09zVV+SR8/M8q+HpxhI2UzmzfV3LcGbdvWSbMBiAaRSc+Tznemn0i5MF3wmrAy/NHeAG3/sRzg2USBbCvCUxguMNDb6aZ0yDMXByzlAsH9Lhqm9/UzlfZJTkown+b59gziWCVSZnxLHMpJaS5oCKircmDZ3u+wdSuEFiifOzZevvVHJifK/Abq7upifn0cI+MjTlzk3s7SkYxXEB/siGIKg9etes6DnUprRN3sOl4OoAmUrApvrTsUKERWpiIMdNTMLdKweuGVjaFJOrFL96UpiKtao+V0TuU1n5U+V8y6EYSpaMxgZnkhJWZWoXctURN1DF27dltWihoA6+lGdqA2LddRultsqf/7/ppz8Xcgb+vv4IfTf/amJ3BQLSCEIgOwSklV1M6c8en7anFRoqn80qyrUOaZCa216IDSKgtsW4LW9zG55LNpCEsQWWrBCpsLz2r++qVwBSISlSushbYmWVk2pZqA9i0vUp0I2mjOaIMrDaSPKnXybJmpbVQ/SMu6TDBN9mxlT4bG49L1y1DU0kJ4fzWIJePs1/SRsyY/dMsLzF7Ns7nZBwGTO57NHp/jQQ+dIWAJbCrKhQ3J8osDh8Ty2NNXyIofi3h3dvHFnL18+Ps3XTs40/TpCTH9TVJvamb/M7dY0iaTN/ibSsWP/anLt/vy79xB8SbC91+Un9m8A4Pknc1we9XjnTQPLPr9jSV67o7nqY2hoiPFxU8zmnw9OlguRLIZA6aZMhdkXlz7WpaJZ0HMp/nizPWy5sFokwV53KlaIZsZzTTOzRHvHAIskane6hGuT6yLWQP7ULFE7KmFqmsG1z/mL0Ox+tTKnIpI61SZqV6QAANXrZ/VZpVWpAb86RLkTEVMR/lCNm98JKcpd4mOPj2yu/+PkmBl/FNYJ7+NS0ncVJkkv1jDqUJ8Xw1Q0MBCjBOAOMRVKAUJiOQ1yB8rVnzrwDAc+geViET8fyrr5DjCxQa4A9CIT8eWKK7kWC1z0NiVqm5yKBr1EovK2rXmIGw8jZPma5VRoxJLyI2pQbn7X/D1BuehFjMMZyZ/CRP9DY3l2DyRJhPdp10Cyrk/Fm3f38vCpWU5PF8l5its2Z/i9Ry9weDzPO/b189CpWW7ZmOHHbxvhU4cm+e5rB+hN2kvKy6g2dF/NCN7/IcRt9zR9jdYmX6LCUFNzGzuZqN2bsJgpLO1kKmhumMs2lcJtap8IEdooTXKLmuRpLhe23Zp7s+5UrBBLkT+1TCPfcAzm55XSp0IpU37nSkrUVkojGuhtonE2YzNaOpZmlH0L6dWyAxF2tFVClJmK2bAiSsSmLVQZmLyGFtwkrReEbUMZFgF55whwa91bVjY/dOXN4RkA/CU0AFNaIIlvxleuhNlGA1orhRayYXNGKYWR1fidkU4EBWOIykYynyjPpANMBaUSgXTKPQ8Wwi4HbjrAVISdmWUqPkIknaip5IJGhm2ITuuwk3Sjst2WJQhkWKmljYiCE/HVn8w9C7SoepCWMWeWEHZVocMQJ1kpz41AUAoURycKvP2a5npw15Lcv7uv5m/PXMhyfrbEj90ywo/cPIITrpk/esvIEr7ENyHyWVNwpgnMsl8972s3mE6WlO1L2Yxll+Zcq2AxpqI9FaKjpPSm522yxjVrgLxcrDMVa4ymidpRpKTND08zLzUaQ0dzBprU/YdFEnHbhMXkT9CcXWklmjIVjsD3W8OalOVPUQdtyyqzF5fCkopW1ZypkT/JFknBow9dkFOBzHEu9fvMFX+X7sSG2nEvEpVpfp6IqTD/8xZhKrTWKF8hGhitkc3W1ukaNO+oapgKq2NMRTA/B1hYyQYR+U5KKv0SSrpYssH9Ka+x7R9KUDAGupWMdyosJyq1R+3DFEZtW8mC6vALNyp/aTmh5LFlCVrxqOQj1R9bFVMBNQtko+sXnd+K2/uioJ4WnJws4CvNvuHU0s8f4mfv2lQ+/zd/KnZzaM8z61Cy+XVUivBWL1j3Q1iWKSvfLCjbKvQmLI6NLy0gE6jmORWyVbLgBTDzq/FeZZoFNn5/M5tiubBb5PB1wJT65oRqQltFkZJ22wJNy/qVI63tHUM1yvq+JkZ8xxPHm8qxotd0xtFRykTvY51A22iIW0ESCFXFVGAStiP2IqKD7SobvFb+1OpIUu3Fj5I4Jwsn6l9Z1cckUCVG51/AVyWK/nzjj6+bdKb6k79Y3Y1iPuwsHn9YyA445YGPErJxYQMrbKrWoZyKYM5c50bGc7k5cpuT1wHDVFguVoP8jrLhqNpPMQYFk+wp0/EGVdR93ZQLX2CZtNjriSpvNXIqZMI11Z9ml1clZ7kohUzgY+dm8RbMh6jwQFCTqL34c1SWPgrBYlR7EJ4/zhA0b9UUrBSjM8ao3NIT7ygvhk7IYl8ViHo8hFX4GsHspQtY6pqO2uZnJ+yA3qTNTDEoJ3w3glYarVgTpqKZfQKLV4XULZQ/tYqpWHcqVgC9iMynU/Kn5ommnWcqgkUm+JWXqN3ZvJNmjEg5ub8Fc6aSUxHKVWwbN58HpZgKE9fKidoLrk3UJHX1iKq4RIOK+lSYX6fyJ+veUT0/Xhr7Fx4+/Tt84tB7+PKJ/9b0NNWfjzD1QBZ1Kmamw4TX+MPl6mntNFoXZSokWtpor0M5FXPGcLBS8VWDOvq8eB6BdGMTcaFinHTCqVDFkN1rdF3cKst2YamqJTqEF2ZLfOrQ5KIJvZH0rJH8SaaSKOmgpyeXdN7l4DNHJvn9Ry+gtebB9NUorfmdr4/yx49frB2DjkrKSpZVUra65vYisimlBVLHJ/H/88FJSlrzcv9eLodOxUjY6O70zGM8deEvFx/LOmpRCJmvReRPpmdvdcmC2oT7Tqk4APqSFkrDfLG5NxAsIdrfLqZCLSK9XsyZaS1T0Zr41beM/Kn3zLmyxtyU3KxMEL3QABF1/yj/U2PWx/s25Nni+ySPynLDrQh9gcbdBN2z82RqLrFe+HFNWOGYA+GfhNZI3yc5H7BxS8DI+QksGX6PUP7Rp+GtW3y6sxapk5WFXYQ/a76urny4Dsv5LfyJUgilTEnQqvdrIdCWhbItbDnGGzbOMliyyJwQCG1eH437temAedui7/R07XiaoGakcfel0fukuS939xcYcn3SRyV6Ac2YKSiGNgr6L+WI+hia76Zrgy3louzmelSuTdxYomtc9Q3Cf1+tfLaN+PSfnClfS0INandRsWe7oufkeFUU0txPXSUhis5tjV6ir1SqGYcOjXg7jKyqcEOf2zDC4IlTjBw+yr1F+GNgu5sjNTmFUygigoDuCxdBwB7pk0pDsjrQKaq2iQVzR8etiEJQ8P3aRO3okJZcG7yW1GyBZGq2cpkEDMsSKuHjzII3cYKtahsCgV20EYUCOhln1C3IqRBRTsViTsUUSlpNu3tDmw3owDfVnxqNIQxO6E4xFVljODSMyJejjB0IVHgllOU2KYYRMRXtj4upUuhUNOhTYUrthjkMtg2lqjKWS8h81Frz/zw2ypHxPFcPJrlupLHhpiL5U0wJ06KvsFIJlBQtdyq8QPHnT10G4A27epmzUggU333tAJ88NMlbr+4vS4zKJWW1qFCBSwluReFrWbWfNnIqkEjira1PH57krQwwnRwgmCvSm7TKJV8Pjn2K2eJ5bhz5HhJ29xK++TqAspxOpBZ3Kmp6jQiwSkXcmVkEMKQ9ruoqkpjWJJMreHabOKcCkH5Aas6UCn6NLPLWXoU1MUUq1djU9T3NNb05dqgcXRetsp0Y2RBaSrY4ReZdRXJ6wfmr9r9yfKt6nJG9tMBuimyf6zJ5ht2AwVcuxZp89/ZrCth0X5irPRCedlshYHBjgZHzMwjH9KWK3ZNrRljzEWXckPQoCU3P+SbKgAibY4qmhPiWcSqSs3NlQ05LUXYkaqZE7HzV5R/VN2FXN+RECmXJGsM5+syRZJHuQh7RoEzySo1lMIaccmxcDWnbJHdoIcoej9AaS2nSdoCrFHYx2tiqDOGF5wzfLk05pIrzEH03KdBCmmsnKtdOKIUslpCBKZ+wOa2wlcQuWVWvDfWvaIadAm62VDOGRq5FbdJjw6sR+6vQGqEUmxKCPC7KshCq/qHanimQmM7DQkchMtSrLhGamusiGixwlffUfq8EUEobIzlasCJnQAlJSYWbZdX50DpsjhOdM/xbqYTr++UxQeUYWuO7Lir0lIo93Uxv20Jibp4g3Bz6LI/+M+dIZCUoSdfYOGhNt4DdI8CpBZ2rl4mx+SxCKey8SXJ1500U3EHyVvVayAEnT9e8Z1AAm4ET8A7eXnOseOoUE/v21Z9oYReu8D4GQuIrjd2IFp6dQgsb0UhG0rGcCtkkB6mSGN0JTXeQNVFdq6uBU9HJ4g9eiUC6uA2+eCRxCTpAtgclH5wmeQzVf69jKuqdCq01Xz4+w/UjaUbnSjx1fp4j43kE8JkjU82dipCpkAuYijPTRX7hC6f4iW0mT0nNzLR0zjx2tmJo/NWzl9moHWwCvuf6QT55aJIj4/mKU6Ejp6KaqVjCpKkJu0ZORbzjEGgrNok/UJrpQoBvaUp2mktzc4xkjJxvunCG2eJ5AMZyR9jas3/xMa3DIMrRWdSpqATgwNgHyZlZ+sO1fgDYuwW4GP/+ViBKyb8buHsrMLb4yd6wEfAaj2vABQaBUy0YYBVu6wulhDoZawf12h6bZB5rrMqpqLI7uoBcRuKUHKxCgFxmObzq7W2nDcoCayL6y8pY4G8Zp+LSDde27LNKRcUXPznL9bek2LW7Xn9cc3xv+2rKHj9S4OChAm+9tQdnQY/5wNd87hMzXPuaJHv2ta8JUjVcu4dPfOwMt9yZZutV9RrWZx/PMnHZ5/7v6u3IeAC++MkZNm11eM3u+sXw/OkSz3wjxxve1k13T/vNtueeyDF2yePbYr7/pQseT7yc5XX7uugfXPyxXG6ZwtzgALnBAf70/HlgDttOo2SWn9mkeVOvYm7DCPMbhjn6coHRo/O86dszZWlBjTy22lmqYZp0zY9SKomSeYJQb150JBkg0AFfl0nGEv9M1hvjlg0/yJnpx5gunCbNVaizbyK5+0t4qsDdW3+KsewR7Eun2F2Kf37LcpFq50GYKjzZbJ7e7vhNUM9Mo6Qb9l6oR6VPRezh1iDwQ/lTA8cmNByDkr+EVn4tGE7oAFrp+GtWZm86oaj0wpyKBo+lEAKbgAAHHQSINnaPjpyKRqeo0fXb9fKnB0/M8MDxaT54/3YuzJV46VKO//PEJZK2pBA6Cd+xtw9LCD53dIq5YkB3gy7NUT7LQqfir58boxRoXhjLcQ1pvlrs555SQFeVVzaV9/lvD5zlpk1p7t7WzesGl34jv3ZyhqQtKPiak1NFbg7ySGeAnoRFT8Li3GwlehYFg5SGx0dz3AHLkz8JixICLR3+9eA0b7zGZkNXZT/RWqOwYssNTxV8NGBLha0lL89qXrvd4cTU13jqwkcRCISwODf7JCm7H0s6SGHR5W5AxjUwjEHBn+WVya/Qm9hCT2IrvcktS3rfqxpLdipgYaDPTyQY27sHgMlxnxefyXPz7Wl6+qsKHNShiUHb5FBffz9TU4ZmH50r8cGvnePdt46wf2vj0r+5rOLAV7NcfUs3W3e6JvioFUJpo7ZQmlcO5rl80eeeN8Z/Ts0eWf5DqDRAhEHZ2j1KAw9+bo7BzQlec3X8dX3ikXkKec19b4ln1Y4dLHD4SIHv/N7ehkGPpeKlZ3KcPVXibe/qW/S1jXmKbyGnopUo2zENE7VDCUabGzOVGxA1bX7X1iHUYLHyZtWJuJ3CYn0qYGlBtFZABWtfhni2ENBjad7VO0/gupzMwpBdoufiJdxsFtvayKznUHRT2I4oMx/lBTZkrsosVLVErmrRnPDP0a27mQzOATAanGCArQgNvrWNm3f/NJ859j6+dPn3ca0MWwb2c2L6IdjxGPhw66YfxevuoSS7mLx8ib1qH0KpcvJ5GTFMhRACoSF7eZze7u0NLsQUSm6NlZGYjxEIFCqbRberPGcQoIXbWP4UVhVqd1fvCCpfBAlWA3qgE2V2y/BKKNmN1aRTsSUUgeVCsbBoAulqUE6OXkQqBxrcBQEc3+d/P2akSC9eyvHbj5xnvqSwpaDgK163o5t33zrCYNrh2ESeTx+Z4vFzc3XlTctjCa+9qLounz86xZPn59k9kOTylMc1Fny0az9/++kTvPvWEUqBxrUEXzk+w9nZIqdnivzr4Sm++2KJH7uxt+zUBkojBXz5+AxDaZvtfQksIbCk4LnRLO/YN8C/HDLfZWdhgoJjxrilx+X8bOUZieRPM0XNh5+e4A7g8VI3D3z1LO+9fUPZQfjCsSmeG83ys3duoith1cifzvtJRoTF3x+e5fOnsnzknXsqrGMQmF4dMUzFZM4wQxkXZgvmGm3smuaZ0b9mKH011w+/k4Pjn+L0zAFOzxwovy9l93Pt0Hexq/+NFPxpTk0/ghAWO3rv4cLcs/SldpCwupnMH+foxJeYKpwMv6vk+pF3krIbG2KXvW7m5+caHn81QPuHSW+12OQmkZ6HVfKM6iMKPIXrsJPXpLvTWPleCALs/hH6b7ybwuQU2eFBhGtRUpKSFmg7KsUMYNQXDRMMQuVBnbRnwb5DMom2jAog3ZXkTElwpiS4JdE4uJvP+sz7NnYilD1ZApA1rlHO0ozli/gNpKErRTGQi+RUNG+Ku1jFzeXAJIWz6op1607FClBdpCIO0mpdy/OljCO++pNAiM4kRFXGEyVqxx9fuz4Vi+nmO3ONmiZqd6hi2Ewh4M6MJiM1Y9u28ImX53j07ByfeOMgfWfPcxcnuGOPxj54zki9lvHZ1fkmw6//CWw3RVJNMN7zN2wcuAMrezMjb4MB7wT2EcFPqJ9FaR+HNGJGUvRux/cVblJiX0rCpaMMaoWnTE+LoSPHjNMQbmZaSrTdBe/8CUgk0cU8WkjmDj0Ds5rsxATsbuBUzEyh3T1Nq2ZIAVoLvONHYGjTMq7EEq+X76NlurGxGjWb8zpTMi0oFCHduAqKVWYqVhcRWxK8EoGVKDvbcZBSEVgJKLXbqQjXtQZMhSUxkU2t6iO5vlfuwvy/D4wyX1L0JS1+7q5NDKRstvW6OCHrsGcgycYuh4dPzTZ2KiL5k23xV89e5tnRLCenity2OcMv37eFzz8+DWfhP536DH9+3Tv4/QOj5fc6UvCTt2/g+pE0X35lmk++eJGgVOTdt42gNPzKl04znvOZzNcuQgMpm0DD63b0kHIkEzmfnmfOUgqdhy09Lk9V6bAj+dPh8WLZCHze7+apC1luu5DlO/Yap+JfDk5ycd7j+OQpfvF1m9nrVja0We2wUSt+8bZefvfpGY6O5yuyML+Ekg6WqF+3J8KxdyUEvXN5kJBJPIwG7t76s6ScPlJOP5ezB0nafSjt4SuPU9OP8MzFv+alsU9QCnJEpu7h8c/iqfryvNcNvYMtPbfz/KW/46XLn4i9V99USMPd/+YtbL44CRcb5+sMAbveeDtwO7x4EL73p1BBQNf4BF3jE2wA9u4CxsP/qqCFQNkWSlpoS5ZLolslD7tUaig3VkKYCocaZBCwMfz7JuC7+i3Gc81z0qLg70KSsRoy9HlbXiJa64ZBJYhKyja2T1TY2qAVY7JtgdbmM1dD/K47FSvAUroYtqo811LG0awPQ6ei8FBxYJpF9NakT0UTJwc6Wf1pcaai3XNmtuizp1ujNHipFDCHxsijlJQUL8wxNumzbXcC2zXaZl2VT1NO2q7OLVHVeSYKlCZ38gn6NqVxxDVs/uFfAECLKYoXTzKz6/X0dNsIDKXrhZtFNkgyMxuwacBBh011A2UxXTpNF90o20bZNjp0LER041QQ/qdxhzfQe8MdiAPPk708hi4VYeKyqVoyMwnpLpibQc9MonqSTSM80jYdrQtf/Rx6/71gO5BIwOQ49A6Yz92yw5RbTKQg8MBJwNwM9A3A+dMwvBFmpmBkE8zOgOtCqQSJBPrwC8DrGm4qwrYAjTp9Ap2/C6YnYXDYfH7/kDmv7UJ+HlIZox0e3gjZeejpg9kpcBNQLJpxT08a43vskhnf9IT5HsU8pLsIcnlINzaey0zF5AT6/Blz3v4h87kjm6CQB8c15UosCYWC+X1qHPoHITtnXh/45qGLat+XCmGEX6NSCfSlC+ixSwRyR0MmCcCW2jgVxw+jB0bMOUsl89nJJOTzlRa+uSw6lwXHQbguulg053YchG2DtAwjpRQimULPTJprGvhG/kSTYIklsIOCccAXVMfJP/EoU3njFEcG759em8e98Cwi0w0nc+ihDTA2Cn2DvDlV4GMXUxx7/Fn29FrouVlEVzd67CJiYJjgqQOwdz+zvuafD4IeDVwAACAASURBVBrj7p3XDvCDNw3hWpLbt3bx9Nkc144d5X/dqDkzUWTjxkFKc/MMJCE1cxCcEX7MmsLePsQnjkxx+NwktlYczVmkLbh92OH1qTlyToq56VmOqG5u7cuxy59kd38JNiZ56okCRE5Ft8tXCgHZUkDGtcryp5NTHjeMmMjuv59/ngetezh3YRy9PYl+8Wmy2T62ph2yvs/ffOMMH9xdCCeaxSw2llbcPGgjxf/P3puHx3FdB76/e6uqV6Ab+0aCOylukkhK1GItlkTFlinLsWVLiaNxHDnjzGSSzHvfyyRxJvkyiZK8xHnO5y+O37PHEy+TyHFiy5blRbYkarEWUhItmqJIkeJOkABB7OhGr1V17/ujuhsLARDcAEG6v+8ju9FdXXXq1F3PPfcc2NU1wpqGSFDmC/kgMeIk4YbLKxWRsKTBzfB79YcZZCdLqq4nktVo3U+171CdWgSOE4T+SdawxG6nu/kMHalXqHZaWKCX8bP0VymoHLfFP8Vg9ihWrIY6WhhRfSz0r0YOSm7tvoHcgenbbCcUwi1e3mSEl5sTkRPUNVkMH3yKQmsbLLsiaEtzGcjn0IUcwrLIFSzyr/2COgaJ3v+buI89TMFxkLd9ALuuiaIreHN3jkVLHeobg+FneWOzVXSRnof0/WCvpu+DBi8SJp9MoGxrwmbn0b2T5b4gXFPDSD6PQJPsPM2masGTmektdW55UuFMP7iHix9wT0SrqcOaQ7BFa7roT+cKSXs+2GO8JaYLr3vO81wacd5djLodTX2MbV/+AWK5QE3tkz27g/hzJ7+bg5Cy02SsnP1JxdQNkn2Z3Z98FUwkUgWfFQ2aPt8CWVp6LTXU+doaTg3H+EVvlujmauLVF9Z6Fv0sPf/8A9Y2LsOrG6Hw427kusXEF71JavcRjqy6l2WLzvYRPX64wBtv5vilzQki0VKODa14Zt8X+XXvU2Qa6snXjrfgqpePox/7GixaDh1HqL3r40QXLAv2VLz0HOqxL04pp3rv/dOvVFgSFU+Se/Jf4MnHLkgX0+ELG7bcfJZ/fOX6jgMU8ffsQr38w+A5lWMMlqLYVSj/XX617BlFHhonz4LboWHqlYrRzesC9ee/e17nnim9Y96rO27GCk/dRZWzR6svf/a8rjGxhp3rb7XifiSThy+FoF2zvZI1Ozm+fHa88BL6mk38t33/Qle0kWovi/Pcy+hJrgPwASvMYzf8MY/sOMIf7vuXcfJoQCcD3/TXzuSRAr7yy8tpjI/uuKm4UVoRaj77+6ybeC9j3v8aUNd2I8+0biYnJPf2H+BXjj+FpdUE54+AvhaLTFLSftDl5H9sR4vt7OxaQUP8OsChY7jAmsZYZaVipKDYsChYQYqfPkZ760lO9eZQ3/x9hp046Zv+Bx/b8wNOxZrY0bge/7G/CAaY9Y0Mn3SxUIT++2+yavN/ZdcrnXz8a/9UmrjaqCt/f1If8oGcx8qG10jXeKjwRta89XVeXhFn8f/6Kar7x5NofJTm0r8yN7ZYuGFBw4nP0zDm8/ITLusyWlMP4an3LVqWhT0bWRovI9ULNeHOt0gdODTtcRKIAXkg/zdvjEZC2/kcxY89iHf7L3MoJXFEhFj9pd/r2dDQQLa01zDWN0Brwadv8FwrFcGrE5quLwhelX9pJxVqGqMnlN2fplupuHRJBK0x3hKhi9gKbCYVF8BMYgOXMyRfbjmmja0sZyfj7Kg853Z/0urSLyFOhdb6HHsqAhlmLfmdr2fg/nTpZRkp+nzmyROcHA6sZSsimh7fpo6z97yVG8+LKTc5dwBRHt/KAiNvvEx4eZy4UEGIQT35ySeb5AkhcW0BHljuJJ2DrhwIgJdJYUVihCyL7M13IfTVgXU8k4Z4VZAULFkbWNeKbeesP3r1RhK3/g9GPB88D53LIKqTgeW4th7ddwYSteAWA6t8sRBkne05DS0Lob/0/WAfNDSPttjZEUS8Bo6AaGqZ9PpWOJhU6F/+BKLvANQ1lFYjWoP7qKoOBlmRaPB3fVPwfbImWE1pbQ96TMeBkTRUJSCXQTQ0o1PDiPrGwBIej6NH0uhCO/RN3WkKIRBCoze+B3FjS+DqM9ALdY3B/VUlgvuTMohrb9mBXhI1gXw19cEqixMal6wQyw5WS5wQsXyWbDSOdsKo4w52bJrBWnUc1b4SecNn0MoPdB8KI4RAF/KIqkRQoB0nWKGJxgM5cpnAymrZQQEqhcwmFA5GDcNDgcy5DFg2+mQ1cmDqkYSUAtsLrOwiWTc6HLdsjv/yb8EpWHb3Vm7yRxCJJNjvh9pGSA+BE0J3dXCmIc+Jwi6qnCZuPSHZFl6P90t/SSiRCFaC6hqC1QyrFd6EvX05NrXGKxMKrTW92QPYoSUAuL/6u4j80aCMpoYR1YmgUlcn0We6ELX1VCmPu2MJ7h7uD/QxEgV5Dbq/F9HYjM5mEPVN6N5u/JoEL3v/i4LMI2+9BTf+MyDF0cFTOPLnVIV+g9c6M6VJhUYhsKRkSV1pg/V1t7IgEWO/bkVsvY+u1ivhCCy86T04ec1Tw3EG7v516n/yTQqxBHmGkFojbt/KJjfLv9qrGL72NmqqIpAZQUXbcJJnB7wYyLlsatvGoPSwl/YysOwqpDpG3c3/AREq+cNbMlgZy2WCsjg0EKzcpVNBOdEKnDCNkWgQzrmQR8Sr0IN9UF2DcJwgw7QKLFbiulsQ06RjPt+gGm9HYtmDWJ//IqKxBf7bX8OxQ2i3GISYjcaDlUFfMdidJf+D79I4vJ/QjVvwX3wSVqxBu0X0Mz/CuuMe4LztHReEchwaHZ++7KVbqfB9jXOBUZEmYzqjJ4yuVEyZWf4yrVRc1HkuhTDvNmbi/mTb4rJv1FZqen+8YA/DbK5UlK87/R4GrUDMRozMSnS7c1heZ2mloljUxKsmv/HRjdqX9ppaa770ajedqSIfW1eP77q0Of0c7s9S89LTWNYVNGd6Uc8eRh94nfqubm4bShN9qRS5KRIN3H6cUOBCU51EhKOB8qQMRqCRWDAoicaCKBf5TqJpRfFEBkYC27N/vJOsm0ZlPOJ7n0X1T9zwJoj3e7R3FpHbo6gxMcwX9GRJFV7FO5lE1Y4ZSAiBPrw/eF+KTpI/eRh/xVUk4jF6lq1Hrrm1crjra44O5kkXfBYkQqjthXNMKgQqEiP63veTKQ0KxgyFx72ei6MDeY4O5qkOWVzREKUmauMXNRwZRjhT5D8oy3blZvzaG3AuYEnaV5rOdJF03qe9JkyiFFVosvtQb+ahLz9tFBFpCXTLIuSGK8557TMjRbrSLjURi0XJcGVPxnRUNTSQ7+sL2s7jw1O6YkFQZwqRKsQ17xn3HJTWHB8sUPAUy+oihMe6UDVOPoEbx8Lxf6rBLNbw1NZOhcIprVR0+Q6V3TdK8XyxhmW1J8msziLr3w8EK3k/7/oqdQ3LGC50UlycoSu9iyDE1xGuXP8+Hn/BY3/dCja0xqF9aXC+BYvR3S6QoT/n8d4r6yoyHBp4kl90P8ziqi1o6056aodoWHYbtgxVdKO0x4G+x2lpu4q66BKiDQ2lcr1y3P2cVTZWruXNnu+R6w3u8eX4z5AqTm3P/8nGG222HfsLNi88wo6TjfyHDY2gFVpILGBxbSnM7JKVtLdcwfN7+ije8wCnjg3DkTMsuuE64jkXnuzg+LXvp/7xh+nP+QjpBauN936IjSNJ/vWJk+y55Ve5bWlQ/9UTKaQt6UwVcaSgMR7UodOpYVoSQSPqVe+jO9pMrVyO/f6PTvn8pkPM4P27gYhdgygUIRpF1DVCXeOkOiiGXPKRZyEXJvzBj5N75TloakOs34T60t8g/vVLtA8uIqYc1HC4tCGA8saASqCNyT7HsoJ+SKnAzdFzg0lfaWQtLIt8bS06Exg08v2D1MTrGcoJXF9P2X6Wx2nONPu3RjOBX7rxVDly4bRu9GW3K//saNVQmddeEi6VC/a7ZlLxhR2nJ03Xrkv/6XHvdSXJXfA6/u9oUbKSGN/Z10fuqKp0lr7SeErjK82yVBSpBT99ZhBKv4dggCuhkmxYEyyBqTHHlDlXI7Y4HSHp2vzZ0x2j9zLmxq7KxznVVeS72/rHfjzmGD3554AsRf2wRbBBc7oBQfmbOhWhGYt/faOPYkidlQqjJmXTQIgvvnw68M+f4jxnfT7DSnPWYRoWE+WVU2meGhmccKzAKQhaCPP4gUFyJ6eeWUya321mIo37ffNImG5VZNfO9KS/byHMa6dGeC43fM5rRaMp8rncOQVL531ePJHmE1c38rH19WQG0nhvHGTRI/8v2ivwgB3C8Yrol4D6JlTDIgZEG42LE0RCGgq5wCrnuYFV/PQpdCEftGZaBWaUXHbcbKjs2DSyrRMI4sJ7uw8wvDv4vHbb1yd1/agp/WP/+PK4DhjmHO5HPV0AuMffYuDpR0g0recbBwf5eddIKYCVpjPtMljya5cCfsVpJDvi8+2fjloRVakR0BquyyY4lsvz8MO78Dxv0lCqFdeUUruh9KirmSptdHeVZqQ4Wr6kgGTYIoTkHup5dP8AR97K4fqamGNhW+D5UO1KbiDJXz17iiPFPImwRcQu1cvSv3IbYktB2JaoUhtkl+rsqVSxcs8hS9BaFSJsC0K2xPU1RV/hl4IZrChGWEKEP3ziBEKWFxMESgdtmq/hvV6SZ48M87XTZ4LzWJJQuePTUPAVBU+RdzUdw4WKfhpjNg1xB6VHdaNKmwL9UtsXfHYMz/dpj4e4lgQ/eGuAk0cLhKwg74gQELElqbzPFdkotdj8t5/2YwlBacsPJ4eLpEpZdKtCktbqENHypndGVzDVmGdWfoZhO9BrzlX4GqK2YGk6SrVn8ZknTxCx5WifUPpdrqD4mBfUxcc6XP5zpXAo9p/J8PGNj/GL7m4S4Vb29X6fgdwxlHY5mXoVS4QQQrKu8cMsrXkvPz70+4SdnThyE//fq93UT0jaVevarCGOj+bqFkXWHSBT7GV397dwZIwTI8/Aqu3sL+Q4fqiW9U33cjK1E1uG8VWR0yOvs7fnezhWhE2L7qM9ehuyZOHpHnkDEEhhkXX70WgEkqhdw4G+H9GeuI6TqVcBSOS2EFYLqI9VE3caWZjs4tmjRf5kWwd3dwxzrRBEbcW+vn/kPcDRweeILDpO1LmRv3jmJJ2pIqvqD3Eq/XOW1X4IAXzr9V6u0YrXunM0JYM6+fihPyQRbScRfpB/3dPH88dTSCG4Ih2lb9jjrzuC6HINMZu26hDdmSDRQAtb6ZaPkyp0sqr+rrMrruG8iNgJyBdQ1dXT5j9RQaUKEtGVl7sFsOF6xJ2/jN72GFcC7Jvc/Q+m/vxcaGBsz5kCEjfehQjfzkDOHReWeCyeq5HW1HloYPS7SxmI71wBf2D8CslkQSsurfuTWak4L17vzlQ6yVE0ICoDfGDM+9FOSlAOMRn8XeMF1ao/59FbcCudXTn8ni2hoBVRZZEt+hMyC6ugMyt1aKJyHTFuw86E/UgTCORWKugkC2MKwdgC6pcO9ScmfRtzzDirVPmL0iCi4Cl8rfEUpYHH2Xoty6qBvIJm4hwfypOW/jjZtYalXpgGQuw/k8OdELnjvBqYaQZ3Y7FKk4qTqSIHM2MG4CXlVmuLFsIc7s/TOViY9Bwzvf5UH1cmkxo+psIcHymwN5sd/4BLfJgGuoaK7EqNnPX7iQhSozkaznHsTYuq+cjawKppF4vkjh3A8gqIB36bYz/fw0uimd/8xPugsZVsn8+eZ0a44b1xYi0zy46gtQ5cT3JB1JSDfT8l8S+PsGJzO7p2AelvvEToA7eSbD/N0I9O0vWBh2i+onG08JUE7zldZM/OHNffGqM6aVe+eLP3MVb3VlMTWURq6YpxP3rj8d9n3fPD+G0LsLo6IVZFofski67YTDJi4/rBwF4KuKIhwq2LEzTEHbZ3pLGOCixLUB2yyvkdS7ot1cccRCzJwpoobrE4rp0Y9yxKH0qCqG9SCCwRvFI676KaMBtb46QKPq91jTCU88HTcAriIcmyRARHCnKewitZ1apcC/pgfWOMzXVxBnM+rlJ4Pnha45YDIwgo+kF9tWQwYShPLlY3RLhuYTU1EYvXujL0ZlwKvqboKSK2IBF2sGWgzaphC12EZNQKBt2lZ2sJiSUD44LMQnXIIhmxKPiakaJPcYwcYUsStSU1Ecn17VVsbI3TPeKy42SavKuQQpT+BRY6WdKTFIH+qmIRcvk8A8OliZAtqIvaFJUmKiQCyHuKlmqHemETykniIWvMREGzqTXOxrY4UVvyyqkRBnMeOU9V2m9Rej7Be1kx8kCQlTrvKWIhC0tAzlXYIignAsiU2nMxpt8I26Kyp+JD1yyCMW7nVzU4WHIYT8HPTvwdAHGniauaP0bBz9BWdTXxUGPl+NbqDZwcfoH7rryVN7rPdhO0SoK+d1mC18/8PcOFkwgk8VATty/5DPv7fsyxo4O0JTbQLx5lZ9dXidhJPJVHa836po9S9LOkC128fOwbHIw+T3vierRW7O19BDWFa6IjY1zd/HGa4+vIuH2M9NxFOZl5fWwFPZn9/OqVdbx6KkO24KGRtLdvoyd7AIB8cZCc18cHVi7gjTNNXNEY5cqWlzg00Mmxoee4+4pf5vWuIBeNsCStiaC9vqLml3grtY171w7xUkcTQ3kfpTWrVJSqiOR3NrXgKc2e7iynUgVuWRz48Dc6m+nrchHN22mr3jjpPRlmji0j6HwBP+JMO6moBIYRAuGXxwACKSXiV34TvfU+tv+0n+oawZUbIqV+sDzDH/teUQ5nHvwrGbDcYrCE64QDt0bbHg0r6fvUVFcx1N8f5AD60mfx+k5TtxgeevYUEVueNfYRAlZko9Qrhz964sTk9wTUFi3WU8XfPtfJkPACQ8gkYzQ9xihV/lyPOWjs35bSfJhGvr2vj7f25yrfV86nNUt1hOtI8DuPHSU7SQjl96gENdj8xvcOU54TjTOUlw3iE6+vNWrccVCLzVbq+OtnT3GK4llGdRjNUbTzD0xGbb76kRXnPmiG9J1x2fFcht+9sZWGpslV+ItXMvT3+vw/dy25ZNedyK4dGYYGfD77/sWTfv/8k2nCEcFv3No46feXmvRgmOeePMP/2NI+aTK544cLvPFajs9/YGllI+7lxC0qfvpoil+5qp7lV5ztmz2S9nn28TS/vbll0mR9l5JCXvHkYyl+ZWMDn1k5+S6op344zJamGn7/+ulSywRcqJ+u5XoUTh8nU9VA4rYP8FLVJp4+Osx/bAquWVnmPQ+LjBAi2KhY2qyYyuZJOjahphiqueSiEY1i14aCiEbVSURt/dknyrnkIxn8RBWibrReLav+KNnC01Rrj58O/j0CQSLchi2j6HCwT8QNgwX4FljZPEk5zN9NUS8ArmiIsq1zmBVNYT55/eT148VtaSxb8H98cMkl84lurQ6uDZDLKradSrH1iloWT5JEc2jA44WnRti6spaWBRef/m5T29QJoADeeC1LZ4fLn93ePuUxT/1wmKXNET553czblLVNcMeymSW8LJfrdMrnuZ+k+cj6ehYsnrxuluX9izumlvf69smTRp0Pr744Qi6j+Jv3TV6etNbs2fsaAAtb68dthv6/roefduVYXnsHXendRJ1a7lz6Z4gpdmeuafggTx97iKuad/Er6+856/uujiKv7chy87JOdvadpDrUSnviOpbV3kbMqeea1l+n75VhqsIO1669lnThNDWRJSjtIUQwOCzLPKj2sv3I13j9zLeAYLLTnthM1KmlrXoDIPBUnlShi+b4esJ2Fcvr7gDgpX3pioW0IbqSjuEdfGi14ONXLUWlq3GPS6h6mYWx64BtrG24h6P2z1iU7OGTG5egtM/39vfSFFtDwR8hVvMi/3HDXagfwj1rGtg/EKyIrGv4CIfTz9Nes4/Prb2uoocnHxumpc3hqhVBtK2tq4I8ym/2vsEbPRBzmgmd+RB3XPMA8fhs+Nq+s9G+jyrkcCOS6XpKpQiifwmB9BUTLTGiOoGfFOTCAtE4fXt0ITgNDYhEqa1uW4Tb18P7b45xoFAO/FG6H0YH8GEkvtSEp3F/Cuvg941Rm2TECib3YqJRdvT9WEPy6N/jvxcK6ID2ZJjapH2W0UoIiI5I6IEbFibwQ2ebDWtP21ieYHNbnHI6i8r5K6+icr7y+WXpSzFGdrsooAOuba3m6mo1zqBe+c2Ee5mMd82k4lIyo43as7KngulDYs5yXoiKXqYodLMebUmX5ZlcoNnMU1HIB9cIR6aukbZ1+cMQ20WXwukT9LRdTQJAjF80KevkQvObaK04M7KPpTI6vjWtJKejkhxrInKKCU3UqUXFF1GVdkg4rSjh0587QtbtZ3P0CmA3RZ0nAuRlnjggUml85WLJyQfjWmuKRY0zzea8y70nqZK3b8r6MnvlE0qRYM8x/pJSzEryu/I1zrWnYjby8Ch/er9nIQS2X1oJjY+fxPQMvw7A6oa72dT6CbRWU04oABpiK2mJX8nBgSe5omFrxTWpIkupbhwfeZKIXcP7l//fWHJ8Nx4KC4oFTciKUx8LjGnWhK5eCMGq5tupleso+hl87RK2qietLzWRs3O9KD9wbwdoqVofyDT0EuuaPhy4+wpQIkdrYiOwDTTUR5fTnz0CwHDhFL4usqzudnLuAK+f+TfyhUFCgEeRYik3hC0cFiSuoWP4ZTa0fLwyKfKnCHqRLp4hatcSdsJA9rK3p+8WZC6HUj5uePoRZaWtqqxUnH38bETGBBAtC/EO7eMTqxMU6mqnPO7ln43gFjX3b5nE0FWiv8dj+7MjPHBVI43NF2/gASgUFE92pLhlSYKlqyY3NHZ3uuzsyfCxNXUka88err/y/AiFvObe6+sm+fX5kcsqtnWkuHlh9aRGrpnyrplU9GcPInQQz15bZYWV5mkTe/XyEnCp1xe6iBahUv2QDLsKIgXCfg1OtogGfCFQykNrD0+GwdYUrSL9mTMEu5LHzI3LO5XHdi7lpT8UQWC2yeRSCFVEyzCgyYk8dshBFDKgFUqGS+uPAlQO5QhcMUR/borEUFohtBvcW8mlKrjhC/DR05p03kFGB8GrRYxkUXYVIrAfY/lZLKpQ4UEG8/3kKz3C2XtJLhXFvEJFsoREAu3VBRFi8NDaRygPXBcRTZPyQvTngh5y9M7H2SAmNI1iwsuYY7VHOHMIy+3GC7fjRpeDkAyOeBAZwbESyLwD+CgEujTEVtKCkEdBQdYtoLVCoyqvSvulvzVa+3ipPoazQ5PctcAudBHKvgUyjNYeJ1WBjuwh8irPrd3XUZXLcKIpz9CpL3EmvQRft9M//Aqx9G5iuePcvDYEuSoyx2XJSiFAhinGr8B3miumDS2C5kOjQRfROKQLnWTcXqJ2EsTYZzuqs1rvdUKZEYQuomQ8OIN0iGqJIx2UHyFIVCEr9aK6eil2uptb2j6NskOAxPfziP4X0OymqINBiGf5ICUyNcLRweeoiSwqyeeRyh7i+Mgecu4AidASipHNROI+2UELy6kFrdD4CC8LVpiqeJaebJKOgQGGUn0I7YEIleTyEF4a364KVkxKz2oyPJXlZOrn9GcPE3XqaKveSCLcRj7nIRM5QkqSGXKQfhbl1IB28bVPNusiqm36CiFUyiq56ggs5SPs6kroTqRD4DbgBflESm2NRtOXeYvO9C8o+Ckaoitpiq9BCBuJRKIQMhQkjbLCpCmiqjwGB4pIK44lBFpEECoTLJcLGysGORGlPxtBqBxKhIJXGRlTSzSun+PQ4FOkCl1E7BoWJq4lGV4wribpkr6V8tAoQlYVOVnLQKqX1IiLTmQZ8W36hmwkAkcXQEQD/QuJY6dRoST92SQi2B2B1oq+7Ft0pF6l4I+woPoaGqPLsGUMUGiVQ8kwSvsIP4sQFogwQtoILIQAqRWBs5RE45OzM0QjQwwNJLBEkCdFaxetBdJP4cswMecwWghe7vsa14159kd7t9HYsJqqUNP4aqAKaOGA9kA4oF2QIdCaFXV38uLJz3N4YBv1kWUgS/H5hUXazSGTXfTm93J13fsIF08DEqHyaGEj0NTFIecmQYcRfg5txc+yPgk/D6U9PxFhI7SLEmOGA9ovnTeHtmKlPqtsFXKJ2/0kw2nCaQ/HrmVBZBmHB35Km50gmuuCkvtrc3hR6XpZ6qPLOJX+OaeGX6UvF0wu6qPLKIaCQK5H+p9iDdCTPYAui+v7rKi7k47hHew78wgN8dV4qoCKueScPl49fgSETVNkMUmnjjPp3SRCjcTFGRwZwnfDoK2z+jThZ0FIhHKDsissIHieyqoa1UFJJ0LlSmMAO3hWIhhYSm8Ip3Bq0no/erFqwvM8ozY9Q3hAyknT3ftDltTchC2jSGEhVFB2LT+D79s4Mo9AYRVTgMYq9mPnjiP9EXw7QSIi6E8n0aX2qlwOhSqCFWze1kqh/BQekpOpnfRk9xNz6mmv3kRjqDFIGuqn8J16hHJLYyIflFeqWxa0NaF9D6v7BNTWUHIIR/pZtHCCOiMdYpxBR0KERk6i7KqgHbaTCFUAFEK5RHWcqpCH9kIIr4DQRSxvGDe8sDK+E6oAwgqujYXQBbQII1QBofIou6p0/RxahtDKwRIudfIY8b4zaBlGqHypnkmUFaWaZhyZwHdDSC8L2keVs7drF6FdmqKdOLl4oD8EWjpBe21XIf1M6T4EWlhoGUJoHy2sYDyMRigXobJo0UhVyMf3Sl4d2i+NEX3QLtqKlnQ9/aRK6ImO2e9QPv/Ulkt+zvJUYZZTLxgM502bCJEUNlft34D/k5/w7a0bia86zs7OLbzZfzX3X/W5S3atEIKPPupS8/5WrIRD9+f2k7hrAfGra+j/9+Mkf6kVu+7clhCNRAsHqQvgLsUu3IgX+xHIVOWY7O4Bhp84Tfcim5YOj3S9RU2hCqumnn/9aP9ZhrJGEaJO2BxVponbIgAAIABJREFUedxZrLkhJEtkhGEUZ1R+1q4L0CrjVMkQx7whvMs2hT+bMJIlVpxhVaRbF2btugDNIkxcSI6p3Kzc8YZnc7S/5bLj49Xc9vXR8vn4g9Xc1dBCq1MX7KBTRQQlYw4WAh8lI0iVRxNMlnwt+ZdiByN66lBwNoIHQ61Epln1KJ9fI9EyHAx2hAVaYfnpYLLg5wjMIgpd8pbXVqQ06LYQ2kPJGFJl0cIOBj5+HsH4vRfdqsD33T5cNBufzrHgsMur/7me+61Guj+3n+pbm8jc0MgjxVOVWlctbH49tBAlHL6RP4Jb8Pnwl1K8fmuEJhmi9bkUTf91NSJi8Yjbwxl9dgK5eCmjRrZ0VgfBvU4jTTKE0gIpdHBfQlQmRhoLqQtoZHDfpcld0NYU0YiSnoKJlZZOaWAmoWQG0mXDX8Us9M7GPR2i759/QcfW5by6anpX0Gu2ZWk75hF+8EHE1/6N48s89twZQ5bMbwrw0OfVFlUJm6z2KJlMZ0Rdl8ft386Q/uB72LbizQlmwrKZN5DBRuAgcYTACcwLpbFdsPdAoUlrHz8oHRUE5VSPF2YclQgahUOTDFGOyze6r0LTpQqMaB93zPnHyg0QwyJWMjiNlW3UtDT2b8bJO9YNTOvgmRRK35Vdo8p6GHvP//WXtk15T++alYo7a3+pYq0IZnTlbLweSDt4FRKEXZrFlZXolWa1BRAhtBDkMy5d/UWcJhcnFEGWwudJ4SCFg4VPfrhAfsijbUEYmzxajvr0a2EFnUtpBURoDy1Do5YR7QdWUdRZ32sZDmba0mG4L8+AgqpGp2J1oZRtGCHJD2QJezEamkPB/ZbucXSm6qKsqkAfldUUEcz0EaWOQyJUAWXFkCofHKPLDalGyUjQ8VhVFHM+p3o1iTYFTgxUMTheCBQWfjaNkypSk5ClRDMi0O24jnHcVqWgiumxn499P7aZUIhSKMOg4wjcNHr7XPLVNtGoQAoZWCYJXpWGbGeOxqQmFtOlzkSUFqhGOwx9VqchxkoHFefK4J7c8AK8cCt2/gRO/hRa2GSyNv2DFokF4EmNwKpsEJUaJD4jAxksXaQmaQcbQRGlDfyB1VQgkASrB2EnTHGyvA0IlAxRqLqytJKgSQqbRLgF6aWRO35KFqhp+Bj3LswwlE5wcECwpf4evHArBR3nF9tdlq6I0dJuBa4MaNA+Tu4o0s8EHXBpVUaUrKPIUKn8haiWIZS9DYSF5zQC+/GdRsAFAR3hX6WuNRk8e52n3Fnn0nlOHuhh0RJJVUIglBesZlgx7JxFVS/kq27Bi5QHqBI3ths4TUu4Ceii2q5GiAj+YC9bk+9DyQJCBYnL7FATDdhghVk70MuJ/hzV7bX4oQi+NxwMotAli1GRQiqHP5xiwYIGhHTQ0kL4uZKFx0dbSWxvmCIgtTfqnzq2nAobKSPUCBtpJ5EqR1oVyBPsOTx+Mo9silCbdNAyhlBZEA6WsNCuYPB4F/X1YWrjOXwRQakCnlWFViMoGSkNVHMlK18My8sEGceFAyjiMkyNjID2eY8Vp+hl8KUNuogvo+Cl8WQE4Q/T2y/JujHqF9fhqwKe0EhVRFux4L60ZuBkH47lUd9ogYggVQ5lVSFVHklg2dLCwgLqZRWOHQUkKbeXgnCCgZ32QDoIrYLVEuEgtU9e+4QcgeeHKeYUnV0WjYti2FEZDESEhfLzlH2BhwYVxcFeWtqcykZqgKSdJCkjaBniBj9LFk1R5bBUEaxqbJUJViOsOEqE0Pjg54LaLiwUEku7gaVRxjl90sUVNdQtsVF4pRwsFlJ7aDuJ1EUYeQwr1MP76u6hyDcrJeDOxB3U1C+k4AerPVqGg/bQigcWURnG8obw7Zqg/hBYBu/2VjDkp1EyWqpvEuHnSOdCpIaKLFq2nnyshTwqaJJlBKGD9v/UoQH8zAArVoGyk0h/JLCWar80UNb4Th1xK0/WC4YAykogvSB2jlRZlBUPrPhWHMvtQ9nJoF9SBbSM8Mb+akSkhhVX1WO5/YTsaj5S6OZMsYeqyM+wOEH7yIfpW1ID7KcQWUEiuYpfE7cw4A9ha02tXUVeOKCKfLDqKnLpPuAHrI2vIRaqx2Ub2cR7kFUx7kCQKQaRnSxCHDxSJFnfytLFyxGqwJA3QNodojbcSkiG6M64nD7YQcvCCInqse23Rmg/uD9dRIsw0k8F/azK44caS9bdYtBna7dSxoP+WQbWZ12oeAWMtrWTU1tbw+DgZKvK8wc5sBf4Besar6KhPkq/24enPVSpzw1WuhzcTI5o8RhC9BOWCQpAVaiJJdWrUMJCqyL5VBHLK1JbG0WiSpb90iR4jBHAsqqx8UlYCdqdBlw0hwtdZPCC5yNCSD9TGhu5IGwcx8F1PUAjlgA8T12fZt2VK0srcaU6iCJYTfDp7RWEIi6hRAhP5XG1j1IFhLBxymMGz0UORqhPgBMNVsqVdJBeanQaIOzKIDzwAAkHk/rKykXQX+vyZN33GRjyycay7FW9BNldxk99aqw64ulqktUhQpFQMGbws8G1hCQ95DEiPJzq0uRHj0bdlKjSStuox4EouV+WdkmUxotBeya0x8hAkZaIRSwWtIVB3yyRQiJVESVLq6vTMC8nFbt37+brX/86Sim2bNnChz/84XP+pr7t1y/Z9U8dL9J9Isvt66qpmiLrcEeuwMETOVZdlSAauzybkvcfSCOk4JqNk294+nlHhr5hn1WbEpfl+hPpPmVxuqOfDdcmJ00k4/uaJx8bpnVhiA3XxS67PJm0z6FX0my8PsbCRWdvL/N9zeMvDRO7MkLbokuf3ZPkDZW3fa/nOH2qwKYbklPmzXitI8PwkM/aq8/9vM53o7YuFpF+DPp7yMeSZEUrT72WQ0dDaIZoaLkfCMLr7ckMEydCc3yCTqqvnvH1fHsXw5ZN1q3FBtzoIuAIAoEr6ynGl5/1m6zn88aZNKHlMdrqxj8vq1CgqvcgbmQduTH+sSqWB57Fla04dDEQaaPGzSP7uqmJbsVvGL+hOFN6PXgiS0dnkQ9cP/XzGIn5PPuLNI2Lm6hrOttKWmYm3qcTg/9GgOKQz/FTaa5dHKO16ezyWSgodj6fIlwfpWlBmAv15E1N+Lt8t/aE146DaTSwqnHqzc1H3gg26K7ccO5NloXSPwi6pomZSSYSZ7RcD3YUOdKZpf3KaqqTk7exbqbAoRM5VlxZTWzCZtyRMe/DzOwZTUXHrhTJGos1DVO4kQJnMs+TQ+GHbiQ8ZlIRqdlCpn7JBV23ZpLPBg/kOX48z5pNSYpT7AfqFzmOdBZouXHqsg0Qq+SpOH+ODaeodSz8cBw/HOT+EJFFtAAqfIwcPfzNsbW0DDv8BeBGlpBuuhcYDTk9dr1OArHYMIofEKrZjCclsI1s8lZETeAvXi4/xaLi2PMp1jVG8ErJEeOlfwBFIC8Uu7qWcPWCKFbDxTz98ViFAn4odH4uwrEG/Oylk2EuEIU3ANjdu4HlN26georwqwf35XFHvsjP6z2+81YrVy+6iwfjIzQt+t3KMW++nuPY4QI33DdZCZ+ccrt99u6e8ZzVLzYcRJ4ZZM2yv5vyN4+/NkTDsjDrF0/dQuWyim27Uqy4NsrixZfmWY6kfY7+vDQ+mSJQTDmYzKobYiycJGDF80+miUcE1199aTa9/3T3MOFFDmvXXfj4bN5NKpRSfPWrX+VP//RPqa+v54//+I+59tprWbhw4bS/2/2jQ6N7RktvzvL8quycHOMnP2FHPiKI/hRXYPXG0SOTd3jhIZd4Jstb284EsYbHhR8bE4JgzJvzdURTgz7VNRa6c2IBCE4UG8qRPePx+mNT73YUnOdFpzqPCDYzVQOiq2rSPBQSWBLO0bfP4/VOWcqfFsTB16W0BxOEG9W9KLn4UdrwLcYdxjgFl56f72sSKR/nTCRY4p5wnFCaRGqE4dcl+49KlH/2pthJm85JysSUx5YYSfs0+BqOV02p8aqBPLlul9e/Lc86ZuK5LdtGee74zk0Em+SDvEFBeFMhIO5oNjVb2FLQPdjDUFUThSOvkI69jpNeRDRyDfrIgYpOaoYzDO+SHHirtHpVul8tJVragbWjvEAqSytOiGCVCEAIIgOaBVaEA+keCrfECbndbNHBuZyuI+jYBNkBK6tIDmfp2SFJ7Q6sKaJcdXxFTfg03R1DnPRKwxINNR0nOdJ0NU/Gb+aPrH081HYv7xs5yNauo5x56uecrllJOSxexaAkBKkhn/qQhJ7slM8jpjWJYpqDP+sjHAnihGut8b2grgpBRcfTPvyxdX/Me8+FWNbHHoiinbOnDJaniWXTnPmFZPitUghTSSW89UyYuMh3dtszRp4BRXWNRJ+Z2v87mskwklK8/rikHFYVOKsOlK87Vvfl6JAQxF9XSleSN5XrdDh0Atd1yWZ0STdpdH7y9is05BLL5jjwdG8lG2zlHsu3Ocl9TtXsj/9wzHrokE/EttFnpu5oG1qa8PwGXn4+xc1jPj/0s2Pkat2gPynXo4qCOKstKZclwai+ynrSOgj4EMsqRG8GPcXALpYpEB0psPtHvUH9maiL0r05znE8z63kFiuXaSFKWXxL2yjGbt4vn0P2+0RCDvrM2QMxnR1hV80Krsj24uUsnm/awJpD/fT95HDlmKruN4kOdNC36g60HQyWZC7FauDQEQ8rZLMUOPjcMdzoQEVuCDarx7IeoeEI+szkgzG7qIhlRziz2yJ9SIzT+bT97CRtefn4RTGPNUmX/cMOHdkZDp0EhEIdFItTGyXmA0tOdRID0v15Xv/xYWLxyQ2lqSFFo1/gh0vvYmmql2P1V5Ld/UXiN74JVQlAEx0uEEvlOfDEmdEfju0LJj6gMQ2srngEcFb/ARAOn6JQGHW1XFG7EH3yTd566tBo/zTxNymX2EgIfWZqw6IslafeNyyyxy6NkdhzNbGshz0YRUcnNxlZueC6x18SnHp1EkNtRhFtmr5tOh+q8mlGDgv29pXucTJHEaDtN6eOUDnv9lQcPHiQ73znO/zJn/wJAI8++igAH/nIR6b93Q/+9A/HfzDx+VwCLcwrRb4LuWzP5zyMVrOPIGEH4TxT3jCR032kGxP0X6FI4RLBIpqvxypMpZ0Jkywx6celL0c/dKVDpGqIbvKVwUq9dAhnq3GLAq3Lk5HpmMkTExRtgRZFhIoBWSKuReveTlRtLYXoBYST1OXO6xIwo0J36UvmtGcsO8zOFhd4e5fK4DGRKc96gZeTQpKwgjo2ojIUS25IQqnxg1MmcY+blgt/SJcv/MXMUAik54LWqHCEmQ7DFJLywULpy1YGzhdL2FRZgTFDoUh7w+f4xfS8Pe5qciaTLZL1EKlhzqxZUMlPMhVKWKhQFk+6CM/B8aNYMwz5ODO9zLwBqxtWhE91U2yqx5eTWRhmdJqpj9PnOmBmFynnjCj3ilKI0dxocjT0qxg3wp9Z/zjj72ei0tIxH/rLqVd+5t1KxcDAAPX1o6G/6uvrOXTo0DS/CNjuXf68CAbD2xK37AAjoKExaIsOwKhDweXYOOwCodK/gMDeOL0/5sVRdnpRvNXYNCqGwXC58ceu7pQnsu/yPqcc6vaCq/y5lgBnEwXu2InEu+zZhkJB39E70xUXwWjbP7uBKcZyFKChIRi1q7dLWZoBmtE5w9swDsCHpvlu3k0qJltYmcxvdNu2bWzbFuxQ/9u//VtG6q657LKVpJml68wO52VRqaztv40QQfkYLTfvrOczEyZuNRdCELLLoVEVBXeGHcV5qs6xbKS08XywJEG4ZT/ISKy0nvR0F1J8LCGRloXrudiWHTxr5SMudLnhgovI+VSA0YuIC603l6Moz8A15PJdvHze2a+r51wzKLlOjHWtGosqmRltKVG+KgUbmnQ576JlnfQ0Z/lLzm07J4TAsgL3Sc/zJ/FtnZqx+VneTt2JX1r/Cbauno9+57pjvDRlQQP+DE4lANsOEXZCFNw8rnc5DUmj16y8maBqiaC8SHF+XYKY9s8LYXJ36sD/UyBL9WRsNnEflKIURoZJKvoFSHGBZXGGP5t3k4r6+nr6+/srf/f391Nbe3ZikzvvvJM777yz8vd//w83zYp872YuNMuz4fwxup49jK5nD6Pr2cPoenYwep49jK7nnnm3hrd8+XJOnz5NT08Pnuexfft2rr322rkWy2AwGAwGg8FgeNcy71YqLMviU5/6FH/913+NUorbb7+d9vb2uRbLYDAYDAaDwWB41zLvJhUAmzZtYtOmTXMthsFgMBgMBoPBYGAeuj8ZDAaDwWAwGAyGtxdmUmEwGAwGg8FgMBguCjOpMBgMBoPBYDAYDBeFmVQYDAaDwWAwGAyGi8JMKgwGg8FgMBgMBsNFYSYVBoPBYDAYDAaD4aIwkwqDwWAwGAwGg8FwUZhJhcFgMBgMBoPBYLgozKTCYDAYDAaDwWAwXBRmUmEwGAwGg8FgMBguCjOpMBgMBoPBYDAYDBeFmVQYDAaDwWAwGAyGi8JMKgwGg8FgMBgMBsNFYSYVBoPBYDAYDAaD4aIwkwqDwWAwGAwGg8FwUZhJhcFgMBgMBoPBYLgozKTCYDAYDAaDwWAwXBRCa63nWgiDwWAwGAwGg8Ewf3lXrFR85jOfmWsR3hUYPc8eRtezh9H17GF0PXsYXc8ORs+zh9H13POumFQYDAaDwWAwGAyGy4eZVBgMBoPBYDAYDIaLwvrzP//zP59rIWaDZcuWzbUI7wqMnmcPo+vZw+h69jC6nj2MrmcHo+fZw+h6bjEbtQ0Gg8FgMBgMBsNFYdyfDIZ5gJn7GwwGg8FgeDvzjphU5HK5uRbhXUM2m51rEd41nD59mgMHDgAghJhjad65mPZj9jDtx+xx5swZuru751qMdwWDg4N4njfXYhgMc8683lORz+f53//7f/Pkk08ipSQSiRCPx+darHck+Xyeb37zmzzxxBMUi0UikQjV1dVzLdY7Es/z+NrXvsb3vvc9VqxYQUtLC5ZlzbVY7zjy+TwPP/wwzz77LI7j4DiOaT8uE+X24yc/+QkjIyOEQiFqamrmWqx3JGVdP/roo1x55ZXU19fPtUjvWMq6fuSRRzhx4gRCCNra2tBaG0PQJSSfz/ONb3wDKSUtLS1zLY5hGub1SsW3v/1tcrkc9957L8eOHeOb3/zmXIv0juTNN9/kL//yL5FScu+993LgwAH27Nkz12K9Y9mzZw/Dw8P8wz/8AzfeeCOO48y1SO84lFJ87WtfA+BDH/oQ3d3dfOtb35pjqd6ZHD9+nL/6q7/Ctm3uv/9+0uk0TzzxxFyL9Y6ks7OThx56iN7eXj73uc+xcuXKuRbpHc3DDz9MPp/nz/7sz1i8eDEvv/wyYFaWLyUDAwN8+ctfZseOHbz44oukUqm5FskwDfNuUuH7PhDMXHO5HB/+8IdZu3Yt9957L1prvvvd786xhO8cysu5dXV1fPrTn+YTn/gEa9euxXEcmpqa5li6dxblcg2QSqUqg4HXX3+dvXv30tfXBwSDYcOFU9bz4OAgHR0d/MZv/AarV69mw4YNHD9+nJ/85CdzLOE7j1gsxvve9z4eeOABVq9ezerVq5FSks/nzV6hS0woFGLFihXcfPPNABw5coSOjg7j4neJUUqRyWRIp9PcfffdJBIJMpkMK1euJJ/PV44xXDhl/YVCIbZu3cqXvvQlMpkMu3fvNrp9GzNv3J86Ozt5+OGHOXLkCC0tLSSTSZ5//nkGBwdZv349oVCIpqYmvv/973PdddcRiUTmWuR5y0RdNzc3U1NTQzqd5n/+z//Jvn37GBkZoaenhwULFhAKheZa5HnLRF3H43F27dpFOp1mcHCQxx57jGKxyHe/+12uueYa4vG4WVq/ACbquaGhgZ07d9LT08PatWvp7e1lYGCAvXv3snnzZtN+XASnT5/mmWeeYfXq1QDE43Gam5uxbRsIJnSvvPIKW7ZsMeX4IplM17lcjt27d/PII4+wd+9eOjo62LFjB+vWrTPl+iIYq2shBKFQiF27dnHo0CEeffRRDh48SDgc5pFHHmHTpk2mrb5ATp8+zbe+9S2OHz9OfX09dXV1JJNJHMfBtm1eeOEFVq1aRVVV1VyLapiEeTGpSKfTfP7zn2fdunXk83l27txJNBrlhhtu4Fvf+hY33XQT4XCYRCJBd3c32WyWpUuXzrXY85KJun7ttdfwPI/29nYcx8GyLH7rt36LhQsXsnfvXjo7O1mzZs1ciz0vmaxcRyIRNm7cyJe//GVisRh/8Ad/wMaNGzl9+jQvvvgiN910k+mkzpPJ9GzbNrfccgv//M//zIkTJ3jiiSe4++67UUoRiUSM3+4F8uKLL/LFL36RN998k3g8zrJly1BKjXPh27dvH77vs3HjxjmUdP4zma4B6uvrOXXqFCtXruQ//af/xLp16zhx4gT79u1j06ZNcyz1/GQqXW/YsIG2tjY6Ojp46KGHuOaaa+jq6uKFF17g5ptvNm31efKjH/2Ihx9+mI0bN9LX18drr73GsmXLKvs3Fy5cyK5duxgeHmblypVIOe+cbd7xzIsn0tnZSTgc5p577uHXfu3X2LBhA9u3b0cpxXXXXcc3vvENAGzbRkpJIpGYW4HnMRN1fdVVV7F3716OHTuGlJLNmzcDQeWOx+NG1xfBVOVaSsndd9/NW2+9VVnmve2226itrTURRi6AyfT86quvEg6H+fu//3tuueUWHnroITZt2kRXVxfJZHKuRZ631NXV8Tu/8zv80R/9EU899RSFQgEpJVrrSlnu6empDMp27txJb2/vXIo8b5lM1xC4m23dupW7774bgKqqKhYtWkRtbe1cijuvmUrXoVCIgYGBcav1t956KzU1NaatvgCqqqr4L//lv7B161Y++clPMjg4WHH9LbuulvvGnp4enn/+ebq6uuZSZMME5sWkYtmyZbiuy9GjR5FSsnr1ahoaGnj11Ve57777OHToEE8++SS7d+9m//79xjpwEUym67q6Ol577bVxx504cYI33niDurq6OZJ0/jOZruvr63nuuee49957SSQSPPPMM7z88sv80z/9E21tbRUXEsPMmUzPjY2NPPPMM4TDYdavX09VVRWHDx8GIBqNzrHE85e1a9eyevVqVq1aRXt7O9/5zneAIM9K2arY0dFBX18fn/3sZ3nhhRdMZLMLZCpdK6XGuYYcO3aMZ5991uyDuwim0jXA+vXr2bFjBy+99BJvvPEGX//6101bfYFs3ryZFStW4Loutm3T3Nxc2Q9UbieWL1+OEILPfOYzPP3006b9eJvxtnJ/UkpNOiEoFApkMhk6OjpYv3498XickZERTp48yTXXXMOyZcvo7e3liSee4AMf+AA33HDDHEg/vzgfXWcyGbq7u1mxYgWZTIZ/+Id/YPv27WzdupXrr79+DqSfX5yPrrPZLB0dHWzcuJHly5eTz+f52c9+xpYtW9iyZcscSD9/uNAy7Xke//7v/84Pf/hD3v/+97N27do5kH5+MZWugcrkoaWlhe9///usWbOmEj42lUrx8MMPk8lkuPPOO7n//vvNJO4cXKiuM5kMX//613n66ae5++67uemmm2ZN5vnK+eo6mUwipWThwoUcOHCA559/nq1bt3LnnXfOptjzjqn0HAqFEEJUJgqPPPJIZeWn/Lvt27fzwgsv8OCDD/LJT37S7K14m/G2mVSUN1G2t7eftfHXcRxc1+XgwYNYlkVraytCCH784x9z66230tzczKpVq9iyZQuLFy+eozuYP1yorrds2VKpwA8++CCLFi2aC/HnFRei68cff5xbb72Vuro62tvbufnmm42uz8HFlOlIJEIoFOKBBx4wep4B0+l6LMlkkqGhIfbs2cPmzZs5fPgwra2tJJNJPvWpT9He3j6LUs9PLlTXhw4doqWlpVKuja7PzcWU63Xr1rFp0ya2bNlidH0OZqJnrTWdnZ0cPHiQe+65B611xTU1Ho9z3333mbb6bcqcTyoKhQLf/e53efHFF7Esi/r6+kmXaaurq3Fdl+9///ts3LiRY8eO0dvby4YNGwiHw3Mg+fzjYnV99dVXE4lETGWeAaZczw6Xqkw3NDTMgfTzi5nqeiyrV6/mK1/5Co888ghVVVWsXbvWBNGYARer6+rqatauXWsCDsyAi9V1IpGohEk2TM1M9VyOmNXd3U1vby+FQoEvfOELxONxli9fbhKUvs2Zs0mF67qVJa5kMslHP/rRSiFqbm4mFouNO95xHJYsWcLw8DA7duxg+/btPPDAA7S1tc2F+POKS6XrBQsWzIX48wpTrmcHU6Znj/PVNQQDg3Q6zRe+8AUsy+L3fu/3uO2228x+t3NgdD17XCpdv/e97zUTimk4Xz2Xy+22bdv48Y9/jBCC++67j/e85z2mTM8DhJ7l7EPpdJpvf/vbeJ7HjTfeyOrVqytLYJ2dnXzve9/juuuu45prrhm30WlsvOdCoWCsuDPA6Hr2MLqeHYyeZ48L1XUZz/PYu3cvGzZsmG3R5x1G17OH0fXscLFt9Z49e+jr6+OOO+6Yq1swXACzulKxc+dO/vEf/5EVK1bQ2trKyy+/TCgUqlgLE4kEfX19HDt2jObm5nHhSsfOUE1UhXNjdD17GF3PDkbPs8fF6BqCgYFlWcb9ZgYYXc8eRtezw8XqWQhBc3OzcZWch8zqpGJ4eJilS5dy1113sXz5cg4cOIDWmiuuuALf95FS0tLSwt69ewmHwxw+fJjh4WFTgS8Ao+vZw+h6djB6nj0uVtfGTWHmGF3PHkbXs8PF6NnoeH5z2Ux2k6WnX716NZ7n4fs+lmXR3NzM0NAQMBqDOJFIUFdXx1e+8hUSiQSf/vSnL5eI7xiMrmcPo+vZweh59jC6nj2MrmcPo+vZwejZMJbLMqkoFosV37mxBU5KOS6E2KFDh7j66qvH/fb48eNs27aNj370o3zwgx+8HOK9ozBb2tD6AAAFtklEQVS6nj2MrmcHo+fZw+h69jC6nj2MrmcHo2fDRC55yIKuri5++7d/m3/7t38DgoIGQdKSMr7v8/+3dz8vUa1xHMc/Z2YaSRr8kUKBkeYUA2ptKgiynYxBG6NNiwgVjCgiaCmBf4HobhZS9IvIhQWtwohaxECECUHWRDQhqIyDRlLOmONpEc693Bu3Zs70Hbm9X6sREZ55czZfz3POs7Kyomw2q9bWVklSIpFQNptVQ0ODhoaGuMh+Aa3t0NoGne3Q2g6t7dDaBp3xIyUfKhzHUX19vcbHx7W4uCifz5ffQydJqVRKfr9fq6ur8vl8mpqa0sDAgJ4+fSrXdRUIBP7z4Bn8hdZ2aG2DznZobYfWdmhtg874Ec8Pav/9InJdV7Ozs9qxY4dqamo0Pj6uI0eOyHEcpdNpxWIxJZNJtbW16c2bN7pz546y2ayi0aiOHTvGW1l+gtZ2aG2DznZobYfWdmhtg874FUUPFblcTjdv3tTk5GT+QRzHcTQzM6Pnz5+rp6dHN27cUGNjo4LBoD58+KCvX7/q9OnT8vv92rJli6qrq9Xb28sBVD9Bazu0tkFnO7S2Q2s7tLZBZxSiqKHCdV1duXJFS0tLamlp0YMHD7S8vKzGxkal02lVVlaqqalJ79690+3bt+X3+xWNRvN76tbW1lRRUaE9e/aU+vv879DaDq1t0NkOre3Q2g6tbdAZhSrqHtTy8rKSyaT6+/u1efNmhUIhTUxMaGJiQnV1dRoZGdGTJ0/kOI62bduWn07X1tbkOA5H2heA1nZobYPOdmhth9Z2aG2DzihUUXcqNm3apJcvX2ppaUm7d+9WdXW1Pn/+rKmpKW3fvl2fPn3S/v371d3drXA4rFu3bqmjo0M+n4+DTQpEazu0tkFnO7S2Q2s7tLZBZxSq6GcqXNfV69evFQ6HFQqFlMvlNDs7q507d+ro0aNqamqS67raunWrotEoF5gHtLZDaxt0tkNrO7S2Q2sbdEYhir43FYlEFAqF9PjxY0lSc3OzEomEcrmcpO8P93BxlQat7dDaBp3t0NoOre3Q2gadUYiih4qamhodOHBAk5OTisfjSqVSCgaD+VeFrR/FDu9obYfWNuhsh9Z2aG2H1jbojEI47voxiEV68eKF4vG4EomEOjs71dnZWaq14R9obYfWNuhsh9Z2aG2H1jbojF/heaiQpNXVVTmOw8RqgNZ2aG2DznZobYfWdmhtg874mZIMFQAAAAD+XLxEGAAAAIAnDBUAAAAAPGGoAAAAAOAJQwUAAAAATxgqAAAAAHjCUAEAMDE2NqZYLFbuZQAAfgNeKQsAKIlTp07lP6+srCgQCMjn+/6/q76+PrW3t5draQCA34yhAgBQcufOndOZM2e0d+/eci8FAGAgUO4FAAD+DKOjo5qbm9OFCxeUSqV0/vx5nT17VqOjo8pkMjp58qR27dqlWCymdDqt9vZ29fb25v/+0aNHun//vj5+/KhwOKy+vj7V19eX8RsBANbxTAUAoGzevn2r4eFhXbx4UdeuXdPY2JguX76swcFBxeNxvXr1SpL07Nkz3b17V5cuXdLIyIgikYiGh4fLvHoAwDqGCgBA2Zw4cULBYFD79u1TRUWFDh8+rKqqKtXW1ioSiej9+/eSpIcPH6qrq0sNDQ3y+/3q6upSMpnU/Px8mb8BAEBi+xMAoIyqqqryn4PB4L9+zmQykqT5+XldvXpV169fz//edV0tLCywBQoANgCGCgDAhldXV6fjx4/zBikA2KDY/gQA2PA6Ojp07949TU9PS5K+fPmieDxe5lUBANZxpwIAsOEdPHhQmUxGQ0NDSqfTqqysVFtbmw4dOlTupQEAxDkVAAAAADxi+xMAAAAATxgqAAAAAHjCUAEAAADAE4YKAAAAAJ4wVAAAAADwhKECAAAAgCcMFQAAAAA8YagAAAAA4AlDBQAAAABPvgEeN7i8annfoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "redd.set_window(start='2011-04-21', end='2011-04-22')\n",
    "elec.plot();\n",
    "plt.xlabel(\"Time\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Autocorrelation Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.plotting import autocorrelation_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elec.mains().plot_autocorrelation();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daily energy consumption across fridges in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    active    1.226223\n",
       "dtype: float64\n",
       "1    active    1.529531\n",
       "dtype: float64\n",
       "2     active    1.04288\n",
       "dtype: float64\n",
       "3    active    0.815836\n",
       "dtype: float64\n",
       "4                                  NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fridges_restricted = nilmtk.global_meter_group.select_using_appliances(type='fridge')\n",
    "daily_energy = pd.Series([meter.average_energy_per_period(offset_alias='D') \n",
    "                          for meter in fridges_restricted.meters])\n",
    "\n",
    "# daily_energy.plot(kind='hist');\n",
    "# plt.title('Histogram of daily fridge energy');\n",
    "# plt.xlabel('energy (kWh)');\n",
    "# plt.ylabel('occurences');\n",
    "# plt.legend().set_visible(False)\n",
    "\n",
    "daily_energy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation dataframe of the appliances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading data for meter ElecMeterID(instance=3, building=1, dataset='REDD')     "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\nilmtk\\nilmtk\\electric.py:376: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  corr = numerator / denominator\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=10, building=1, dataset='REDD')      ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=10, building=1, dataset='REDD')      ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=3, building=1, dataset='REDD')      ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
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      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
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      "Done loading data all meters for this chunk.\n",
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      "Done loading data all meters for this chunk.\n",
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      "Loading data for meter ElecMeterID(instance=10, building=1, dataset='REDD')      ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
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      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
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      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
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      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=10, building=1, dataset='REDD')      ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
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      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
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     "data": {
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       "      <th></th>\n",
       "      <th>(1, 1, REDD)</th>\n",
       "      <th>(2, 1, REDD)</th>\n",
       "      <th>(5, 1, REDD)</th>\n",
       "      <th>(6, 1, REDD)</th>\n",
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       "      <th>(15, 1, REDD)</th>\n",
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       "      <th>(((10, 1, REDD), (20, 1, REDD)),)</th>\n",
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       "      <th>(1, 1, REDD)</th>\n",
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       "      <td>0.288833</td>\n",
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       "      <td>0.0212352</td>\n",
       "      <td>0.144032</td>\n",
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       "      <td>0.117099</td>\n",
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       "      <td>0.221559</td>\n",
       "      <td>0.749084</td>\n",
       "      <td>0.737687</td>\n",
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       "      <td>0.00256023</td>\n",
       "      <td>-0.0340499</td>\n",
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       "    <tr>\n",
       "      <th>(5, 1, REDD)</th>\n",
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       "      <td>0.108847</td>\n",
       "      <td>0.00518485</td>\n",
       "      <td>0.0879256</td>\n",
       "      <td>0.0613822</td>\n",
       "      <td>0.0946321</td>\n",
       "      <td>-0.0270217</td>\n",
       "      <td>0.0298573</td>\n",
       "      <td>-0.00203541</td>\n",
       "      <td>0.00180875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.121275</td>\n",
       "      <td>0.411608</td>\n",
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       "    <tr>\n",
       "      <th>(6, 1, REDD)</th>\n",
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       "      <td>0.0112727</td>\n",
       "      <td>-0.0120231</td>\n",
       "      <td>-0.00784038</td>\n",
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       "      <td>0.130844</td>\n",
       "      <td>0.558686</td>\n",
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       "      <td>-0.0124059</td>\n",
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       "      <td>0.00548738</td>\n",
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       "      <td>0.0344461</td>\n",
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       "      <td>0.027573</td>\n",
       "      <td>0.131574</td>\n",
       "      <td>0.101213</td>\n",
       "      <td>0.0626436</td>\n",
       "      <td>0.0524957</td>\n",
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       "      <td>-0.0106104</td>\n",
       "      <td>-0.0446488</td>\n",
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       "      <td>0.052323</td>\n",
       "      <td>0.0451693</td>\n",
       "      <td>0.206243</td>\n",
       "      <td>0.187374</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.0069217</td>\n",
       "      <td>0.0612987</td>\n",
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       "    <tr>\n",
       "      <th>(11, 1, REDD)</th>\n",
       "      <td>0.674169</td>\n",
       "      <td>0.117099</td>\n",
       "      <td>0.00518485</td>\n",
       "      <td>0.0292081</td>\n",
       "      <td>-0.00587421</td>\n",
       "      <td>0.0356909</td>\n",
       "      <td>-0.0484712</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00875737</td>\n",
       "      <td>0.748305</td>\n",
       "      <td>0.755141</td>\n",
       "      <td>0.0393549</td>\n",
       "      <td>0.37349</td>\n",
       "      <td>0.135711</td>\n",
       "      <td>0.144454</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00169212</td>\n",
       "      <td>0.0343062</td>\n",
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       "    <tr>\n",
       "      <th>(12, 1, REDD)</th>\n",
       "      <td>0.435024</td>\n",
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       "      <td>0.0879256</td>\n",
       "      <td>-0.00568256</td>\n",
       "      <td>0.00151782</td>\n",
       "      <td>-0.0201429</td>\n",
       "      <td>0.0486186</td>\n",
       "      <td>0.00875737</td>\n",
       "      <td>1</td>\n",
       "      <td>0.653199</td>\n",
       "      <td>0.62843</td>\n",
       "      <td>0.000221234</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.147108</td>\n",
       "      <td>0.119364</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.000819459</td>\n",
       "      <td>-0.00263386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(13, 1, REDD)</th>\n",
       "      <td>0.784056</td>\n",
       "      <td>0.143877</td>\n",
       "      <td>0.0613822</td>\n",
       "      <td>0.020327</td>\n",
       "      <td>-0.00177207</td>\n",
       "      <td>0.0127344</td>\n",
       "      <td>-0.00604461</td>\n",
       "      <td>0.748305</td>\n",
       "      <td>0.653199</td>\n",
       "      <td>1</td>\n",
       "      <td>0.98348</td>\n",
       "      <td>0.0303775</td>\n",
       "      <td>0.607367</td>\n",
       "      <td>0.1968</td>\n",
       "      <td>0.185798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00188285</td>\n",
       "      <td>0.0228828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(14, 1, REDD)</th>\n",
       "      <td>0.831063</td>\n",
       "      <td>0.162691</td>\n",
       "      <td>0.0946321</td>\n",
       "      <td>0.0725096</td>\n",
       "      <td>0.00664499</td>\n",
       "      <td>0.027573</td>\n",
       "      <td>0.00686124</td>\n",
       "      <td>0.755141</td>\n",
       "      <td>0.62843</td>\n",
       "      <td>0.98348</td>\n",
       "      <td>1</td>\n",
       "      <td>0.170195</td>\n",
       "      <td>0.7024</td>\n",
       "      <td>0.209405</td>\n",
       "      <td>0.199882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00895585</td>\n",
       "      <td>0.0805764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(15, 1, REDD)</th>\n",
       "      <td>0.315794</td>\n",
       "      <td>0.178104</td>\n",
       "      <td>-0.0270217</td>\n",
       "      <td>-0.00771563</td>\n",
       "      <td>-0.0170461</td>\n",
       "      <td>0.131574</td>\n",
       "      <td>0.052323</td>\n",
       "      <td>0.0393549</td>\n",
       "      <td>0.000221234</td>\n",
       "      <td>0.0303775</td>\n",
       "      <td>0.170195</td>\n",
       "      <td>1</td>\n",
       "      <td>0.774916</td>\n",
       "      <td>0.131523</td>\n",
       "      <td>0.135678</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00111699</td>\n",
       "      <td>-0.00431885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(16, 1, REDD)</th>\n",
       "      <td>0.681408</td>\n",
       "      <td>0.221559</td>\n",
       "      <td>0.0298573</td>\n",
       "      <td>0.0112727</td>\n",
       "      <td>-0.0124059</td>\n",
       "      <td>0.101213</td>\n",
       "      <td>0.0451693</td>\n",
       "      <td>0.37349</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.607367</td>\n",
       "      <td>0.7024</td>\n",
       "      <td>0.774916</td>\n",
       "      <td>1</td>\n",
       "      <td>0.223816</td>\n",
       "      <td>0.217696</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00582928</td>\n",
       "      <td>0.0176151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(17, 1, REDD)</th>\n",
       "      <td>0.288833</td>\n",
       "      <td>0.749084</td>\n",
       "      <td>-0.00203541</td>\n",
       "      <td>-0.0120231</td>\n",
       "      <td>0.0058632</td>\n",
       "      <td>0.0626436</td>\n",
       "      <td>0.206243</td>\n",
       "      <td>0.135711</td>\n",
       "      <td>0.147108</td>\n",
       "      <td>0.1968</td>\n",
       "      <td>0.209405</td>\n",
       "      <td>0.131523</td>\n",
       "      <td>0.223816</td>\n",
       "      <td>1</td>\n",
       "      <td>0.993354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.011452</td>\n",
       "      <td>-0.0128027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(18, 1, REDD)</th>\n",
       "      <td>0.283513</td>\n",
       "      <td>0.737687</td>\n",
       "      <td>0.00180875</td>\n",
       "      <td>-0.00784038</td>\n",
       "      <td>0.00548738</td>\n",
       "      <td>0.0524957</td>\n",
       "      <td>0.187374</td>\n",
       "      <td>0.144454</td>\n",
       "      <td>0.119364</td>\n",
       "      <td>0.185798</td>\n",
       "      <td>0.199882</td>\n",
       "      <td>0.135678</td>\n",
       "      <td>0.217696</td>\n",
       "      <td>0.993354</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0103581</td>\n",
       "      <td>-0.00827743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(19, 1, REDD)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(((3, 1, REDD), (4, 1, REDD)),)</th>\n",
       "      <td>0.0212352</td>\n",
       "      <td>0.00256023</td>\n",
       "      <td>0.121275</td>\n",
       "      <td>0.130844</td>\n",
       "      <td>0.0344461</td>\n",
       "      <td>-0.0106104</td>\n",
       "      <td>-0.0069217</td>\n",
       "      <td>-0.00169212</td>\n",
       "      <td>-0.000819459</td>\n",
       "      <td>-0.00188285</td>\n",
       "      <td>0.00895585</td>\n",
       "      <td>-0.00111699</td>\n",
       "      <td>0.00582928</td>\n",
       "      <td>0.011452</td>\n",
       "      <td>0.0103581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.161585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(((10, 1, REDD), (20, 1, REDD)),)</th>\n",
       "      <td>0.144032</td>\n",
       "      <td>-0.0340499</td>\n",
       "      <td>0.411608</td>\n",
       "      <td>0.558686</td>\n",
       "      <td>0.138726</td>\n",
       "      <td>-0.0446488</td>\n",
       "      <td>0.0612987</td>\n",
       "      <td>0.0343062</td>\n",
       "      <td>-0.00263386</td>\n",
       "      <td>0.0228828</td>\n",
       "      <td>0.0805764</td>\n",
       "      <td>-0.00431885</td>\n",
       "      <td>0.0176151</td>\n",
       "      <td>-0.0128027</td>\n",
       "      <td>-0.00827743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.161585</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  (1, 1, REDD) (2, 1, REDD) (5, 1, REDD)  \\\n",
       "(1, 1, REDD)                                 1     0.133881     0.377359   \n",
       "(2, 1, REDD)                          0.133881            1   -0.0937125   \n",
       "(5, 1, REDD)                          0.377359   -0.0937125            1   \n",
       "(6, 1, REDD)                          0.162288   -0.0359251     0.544081   \n",
       "(7, 1, REDD)                       -0.00471913     0.249301     0.214719   \n",
       "(8, 1, REDD)                        -0.0996323     0.702927    -0.145134   \n",
       "(9, 1, REDD)                          0.329465   -0.0932022     0.108847   \n",
       "(11, 1, REDD)                         0.674169     0.117099   0.00518485   \n",
       "(12, 1, REDD)                         0.435024    0.0877732    0.0879256   \n",
       "(13, 1, REDD)                         0.784056     0.143877    0.0613822   \n",
       "(14, 1, REDD)                         0.831063     0.162691    0.0946321   \n",
       "(15, 1, REDD)                         0.315794     0.178104   -0.0270217   \n",
       "(16, 1, REDD)                         0.681408     0.221559    0.0298573   \n",
       "(17, 1, REDD)                         0.288833     0.749084  -0.00203541   \n",
       "(18, 1, REDD)                         0.283513     0.737687   0.00180875   \n",
       "(19, 1, REDD)                              NaN          NaN          NaN   \n",
       "(((3, 1, REDD), (4, 1, REDD)),)      0.0212352   0.00256023     0.121275   \n",
       "(((10, 1, REDD), (20, 1, REDD)),)     0.144032   -0.0340499     0.411608   \n",
       "\n",
       "                                  (6, 1, REDD) (7, 1, REDD) (8, 1, REDD)  \\\n",
       "(1, 1, REDD)                          0.162288  -0.00471913   -0.0996323   \n",
       "(2, 1, REDD)                        -0.0359251     0.249301     0.702927   \n",
       "(5, 1, REDD)                          0.544081     0.214719    -0.145134   \n",
       "(6, 1, REDD)                                 1     0.211379   -0.0492527   \n",
       "(7, 1, REDD)                          0.211379            1     0.358102   \n",
       "(8, 1, REDD)                        -0.0492527     0.358102            1   \n",
       "(9, 1, REDD)                         0.0423541    -0.135737    -0.352982   \n",
       "(11, 1, REDD)                        0.0292081  -0.00587421    0.0356909   \n",
       "(12, 1, REDD)                      -0.00568256   0.00151782   -0.0201429   \n",
       "(13, 1, REDD)                         0.020327  -0.00177207    0.0127344   \n",
       "(14, 1, REDD)                        0.0725096   0.00664499     0.027573   \n",
       "(15, 1, REDD)                      -0.00771563   -0.0170461     0.131574   \n",
       "(16, 1, REDD)                        0.0112727   -0.0124059     0.101213   \n",
       "(17, 1, REDD)                       -0.0120231    0.0058632    0.0626436   \n",
       "(18, 1, REDD)                      -0.00784038   0.00548738    0.0524957   \n",
       "(19, 1, REDD)                              NaN          NaN          NaN   \n",
       "(((3, 1, REDD), (4, 1, REDD)),)       0.130844    0.0344461   -0.0106104   \n",
       "(((10, 1, REDD), (20, 1, REDD)),)     0.558686     0.138726   -0.0446488   \n",
       "\n",
       "                                  (9, 1, REDD) (11, 1, REDD) (12, 1, REDD)  \\\n",
       "(1, 1, REDD)                          0.329465      0.674169      0.435024   \n",
       "(2, 1, REDD)                        -0.0932022      0.117099     0.0877732   \n",
       "(5, 1, REDD)                          0.108847    0.00518485     0.0879256   \n",
       "(6, 1, REDD)                         0.0423541     0.0292081   -0.00568256   \n",
       "(7, 1, REDD)                         -0.135737   -0.00587421    0.00151782   \n",
       "(8, 1, REDD)                         -0.352982     0.0356909    -0.0201429   \n",
       "(9, 1, REDD)                                 1    -0.0484712     0.0486186   \n",
       "(11, 1, REDD)                       -0.0484712             1    0.00875737   \n",
       "(12, 1, REDD)                        0.0486186    0.00875737             1   \n",
       "(13, 1, REDD)                      -0.00604461      0.748305      0.653199   \n",
       "(14, 1, REDD)                       0.00686124      0.755141       0.62843   \n",
       "(15, 1, REDD)                         0.052323     0.0393549   0.000221234   \n",
       "(16, 1, REDD)                        0.0451693       0.37349      0.480808   \n",
       "(17, 1, REDD)                         0.206243      0.135711      0.147108   \n",
       "(18, 1, REDD)                         0.187374      0.144454      0.119364   \n",
       "(19, 1, REDD)                              NaN           NaN           NaN   \n",
       "(((3, 1, REDD), (4, 1, REDD)),)     -0.0069217   -0.00169212  -0.000819459   \n",
       "(((10, 1, REDD), (20, 1, REDD)),)    0.0612987     0.0343062   -0.00263386   \n",
       "\n",
       "                                  (13, 1, REDD) (14, 1, REDD) (15, 1, REDD)  \\\n",
       "(1, 1, REDD)                           0.784056      0.831063      0.315794   \n",
       "(2, 1, REDD)                           0.143877      0.162691      0.178104   \n",
       "(5, 1, REDD)                          0.0613822     0.0946321    -0.0270217   \n",
       "(6, 1, REDD)                           0.020327     0.0725096   -0.00771563   \n",
       "(7, 1, REDD)                        -0.00177207    0.00664499    -0.0170461   \n",
       "(8, 1, REDD)                          0.0127344      0.027573      0.131574   \n",
       "(9, 1, REDD)                        -0.00604461    0.00686124      0.052323   \n",
       "(11, 1, REDD)                          0.748305      0.755141     0.0393549   \n",
       "(12, 1, REDD)                          0.653199       0.62843   0.000221234   \n",
       "(13, 1, REDD)                                 1       0.98348     0.0303775   \n",
       "(14, 1, REDD)                           0.98348             1      0.170195   \n",
       "(15, 1, REDD)                         0.0303775      0.170195             1   \n",
       "(16, 1, REDD)                          0.607367        0.7024      0.774916   \n",
       "(17, 1, REDD)                            0.1968      0.209405      0.131523   \n",
       "(18, 1, REDD)                          0.185798      0.199882      0.135678   \n",
       "(19, 1, REDD)                               NaN           NaN           NaN   \n",
       "(((3, 1, REDD), (4, 1, REDD)),)     -0.00188285    0.00895585   -0.00111699   \n",
       "(((10, 1, REDD), (20, 1, REDD)),)     0.0228828     0.0805764   -0.00431885   \n",
       "\n",
       "                                  (16, 1, REDD) (17, 1, REDD) (18, 1, REDD)  \\\n",
       "(1, 1, REDD)                           0.681408      0.288833      0.283513   \n",
       "(2, 1, REDD)                           0.221559      0.749084      0.737687   \n",
       "(5, 1, REDD)                          0.0298573   -0.00203541    0.00180875   \n",
       "(6, 1, REDD)                          0.0112727    -0.0120231   -0.00784038   \n",
       "(7, 1, REDD)                         -0.0124059     0.0058632    0.00548738   \n",
       "(8, 1, REDD)                           0.101213     0.0626436     0.0524957   \n",
       "(9, 1, REDD)                          0.0451693      0.206243      0.187374   \n",
       "(11, 1, REDD)                           0.37349      0.135711      0.144454   \n",
       "(12, 1, REDD)                          0.480808      0.147108      0.119364   \n",
       "(13, 1, REDD)                          0.607367        0.1968      0.185798   \n",
       "(14, 1, REDD)                            0.7024      0.209405      0.199882   \n",
       "(15, 1, REDD)                          0.774916      0.131523      0.135678   \n",
       "(16, 1, REDD)                                 1      0.223816      0.217696   \n",
       "(17, 1, REDD)                          0.223816             1      0.993354   \n",
       "(18, 1, REDD)                          0.217696      0.993354             1   \n",
       "(19, 1, REDD)                               NaN           NaN           NaN   \n",
       "(((3, 1, REDD), (4, 1, REDD)),)      0.00582928      0.011452     0.0103581   \n",
       "(((10, 1, REDD), (20, 1, REDD)),)     0.0176151    -0.0128027   -0.00827743   \n",
       "\n",
       "                                  (19, 1, REDD)  \\\n",
       "(1, 1, REDD)                                NaN   \n",
       "(2, 1, REDD)                                NaN   \n",
       "(5, 1, REDD)                                NaN   \n",
       "(6, 1, REDD)                                NaN   \n",
       "(7, 1, REDD)                                NaN   \n",
       "(8, 1, REDD)                                NaN   \n",
       "(9, 1, REDD)                                NaN   \n",
       "(11, 1, REDD)                               NaN   \n",
       "(12, 1, REDD)                               NaN   \n",
       "(13, 1, REDD)                               NaN   \n",
       "(14, 1, REDD)                               NaN   \n",
       "(15, 1, REDD)                               NaN   \n",
       "(16, 1, REDD)                               NaN   \n",
       "(17, 1, REDD)                               NaN   \n",
       "(18, 1, REDD)                               NaN   \n",
       "(19, 1, REDD)                               NaN   \n",
       "(((3, 1, REDD), (4, 1, REDD)),)             NaN   \n",
       "(((10, 1, REDD), (20, 1, REDD)),)           NaN   \n",
       "\n",
       "                                  (((3, 1, REDD), (4, 1, REDD)),)  \\\n",
       "(1, 1, REDD)                                            0.0212352   \n",
       "(2, 1, REDD)                                           0.00256023   \n",
       "(5, 1, REDD)                                             0.121275   \n",
       "(6, 1, REDD)                                             0.130844   \n",
       "(7, 1, REDD)                                            0.0344461   \n",
       "(8, 1, REDD)                                           -0.0106104   \n",
       "(9, 1, REDD)                                           -0.0069217   \n",
       "(11, 1, REDD)                                         -0.00169212   \n",
       "(12, 1, REDD)                                        -0.000819459   \n",
       "(13, 1, REDD)                                         -0.00188285   \n",
       "(14, 1, REDD)                                          0.00895585   \n",
       "(15, 1, REDD)                                         -0.00111699   \n",
       "(16, 1, REDD)                                          0.00582928   \n",
       "(17, 1, REDD)                                            0.011452   \n",
       "(18, 1, REDD)                                           0.0103581   \n",
       "(19, 1, REDD)                                                 NaN   \n",
       "(((3, 1, REDD), (4, 1, REDD)),)                                 1   \n",
       "(((10, 1, REDD), (20, 1, REDD)),)                        0.161585   \n",
       "\n",
       "                                  (((10, 1, REDD), (20, 1, REDD)),)  \n",
       "(1, 1, REDD)                                               0.144032  \n",
       "(2, 1, REDD)                                             -0.0340499  \n",
       "(5, 1, REDD)                                               0.411608  \n",
       "(6, 1, REDD)                                               0.558686  \n",
       "(7, 1, REDD)                                               0.138726  \n",
       "(8, 1, REDD)                                             -0.0446488  \n",
       "(9, 1, REDD)                                              0.0612987  \n",
       "(11, 1, REDD)                                             0.0343062  \n",
       "(12, 1, REDD)                                           -0.00263386  \n",
       "(13, 1, REDD)                                             0.0228828  \n",
       "(14, 1, REDD)                                             0.0805764  \n",
       "(15, 1, REDD)                                           -0.00431885  \n",
       "(16, 1, REDD)                                             0.0176151  \n",
       "(17, 1, REDD)                                            -0.0128027  \n",
       "(18, 1, REDD)                                           -0.00827743  \n",
       "(19, 1, REDD)                                                   NaN  \n",
       "(((3, 1, REDD), (4, 1, REDD)),)                            0.161585  \n",
       "(((10, 1, REDD), (20, 1, REDD)),)                                 1  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_df = elec.pairwise_correlation()\n",
    "correlation_df"
   ]
  }
 ],
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